library(ggplot2)
library(corrplot)
library(kernlab)
library(dplyr)
library(ade4)
library(ROCR)
library(caret)
library(DescTools)
library(imbalance)
library(rpart)
library(e1071)
library(randomForest)
library(pROC)
library(explor)
#setwd("~/Documents/IMT Atlantique/2A/S4B1/H - Analyse des données de santé - épidémiologie et aide à la décision/Stroke") ### chemin a changer ici
#getwd()
data <- read.csv("healthcare-dataset-stroke-data.csv", header=TRUE, sep=",")
head(data)
str(data)
'data.frame': 5110 obs. of 12 variables:
$ id : int 9046 51676 31112 60182 1665 56669 53882 10434 27419 60491 ...
$ gender : chr "Male" "Female" "Male" "Female" ...
$ age : num 67 61 80 49 79 81 74 69 59 78 ...
$ hypertension : int 0 0 0 0 1 0 1 0 0 0 ...
$ heart_disease : int 1 0 1 0 0 0 1 0 0 0 ...
$ ever_married : chr "Yes" "Yes" "Yes" "Yes" ...
$ work_type : chr "Private" "Self-employed" "Private" "Private" ...
$ Residence_type : chr "Urban" "Rural" "Rural" "Urban" ...
$ avg_glucose_level: num 229 202 106 171 174 ...
$ bmi : chr "36.6" "N/A" "32.5" "34.4" ...
$ smoking_status : chr "formerly smoked" "never smoked" "never smoked" "smokes" ...
$ stroke : int 1 1 1 1 1 1 1 1 1 1 ...
data_format <- data
str(data_format)
'data.frame': 5110 obs. of 12 variables:
$ id : int 9046 51676 31112 60182 1665 56669 53882 10434 27419 60491 ...
$ gender : chr "Male" "Female" "Male" "Female" ...
$ age : num 67 61 80 49 79 81 74 69 59 78 ...
$ hypertension : int 0 0 0 0 1 0 1 0 0 0 ...
$ heart_disease : int 1 0 1 0 0 0 1 0 0 0 ...
$ ever_married : chr "Yes" "Yes" "Yes" "Yes" ...
$ work_type : chr "Private" "Self-employed" "Private" "Private" ...
$ Residence_type : chr "Urban" "Rural" "Rural" "Urban" ...
$ avg_glucose_level: num 229 202 106 171 174 ...
$ bmi : chr "36.6" "N/A" "32.5" "34.4" ...
$ smoking_status : chr "formerly smoked" "never smoked" "never smoked" "smokes" ...
$ stroke : int 1 1 1 1 1 1 1 1 1 1 ...
nrow(data_format)
[1] 5110
# supprime id
data_format<- data_format[,-c(1)]
# conversion de bmi chr en num
data_format$bmi <- suppressWarnings(as.numeric(data_format$bmi))
# supprime le cas ou genre = other (une fois)
data_format<-data_format[!(data_format$gender=="Other"),]
data_format<-data_format[!(data_format$smoking_status=="Unknown"),]
# assemble deux facteur d'un attribut en un (pas assez nombreux pour deux)
data_format$work_type[data_format$work_type == "children" | data_format$work_type == "Never_worked"] <- "never_worked"
# conversion chr en facteur
data_format <- as.data.frame(unclass(data_format), stringsAsFactors = TRUE)
# supprime les na
data_format <- na.omit(data_format)
# les binaires (hypertension, heart_disease et stroke) en facteur
data_format$hypertension <- factor(as.factor(data_format$hypertension),labels=c("htN","htP"))
data_format$heart_disease <- factor(as.factor(data_format$heart_disease),labels=c("hdN","hdP"))
data_format$stroke <- factor(as.factor(data_format$stroke),labels=c("avcN","avcP"))
# change les labels qui ne sont pas utilisables en R
data_format$work_type <- factor(as.factor(data_format$work_type),labels=c("never_worked","govt_job","private","self_employed"))
data_format$smoking_status <- factor(as.factor(data_format$smoking_status),labels=c("formerly","never","smokes"))
str(data_format)
'data.frame': 3425 obs. of 11 variables:
$ gender : Factor w/ 2 levels "Female","Male": 2 2 1 1 2 2 1 1 1 1 ...
$ age : num 67 80 49 79 81 74 69 81 61 54 ...
$ hypertension : Factor w/ 2 levels "htN","htP": 1 1 1 2 1 2 1 2 1 1 ...
$ heart_disease : Factor w/ 2 levels "hdN","hdP": 2 2 1 1 1 2 1 1 2 1 ...
$ ever_married : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 1 2 2 2 ...
$ work_type : Factor w/ 4 levels "never_worked",..: 3 3 3 4 3 3 3 3 1 3 ...
$ Residence_type : Factor w/ 2 levels "Rural","Urban": 2 1 2 1 2 1 2 1 1 2 ...
$ avg_glucose_level: num 229 106 171 174 186 ...
$ bmi : num 36.6 32.5 34.4 24 29 27.4 22.8 29.7 36.8 27.3 ...
$ smoking_status : Factor w/ 3 levels "formerly","never",..: 1 2 3 2 1 2 2 2 3 3 ...
$ stroke : Factor w/ 2 levels "avcN","avcP": 2 2 2 2 2 2 2 2 2 2 ...
- attr(*, "na.action")= 'omit' Named int [1:140] 2 24 36 42 45 48 59 67 85 92 ...
..- attr(*, "names")= chr [1:140] "2" "24" "36" "42" ...
nrow(data_format)
[1] 3425
# Variable - Age
# Cree un data set avec AVc = 0 et un autre = 1
avc0 <- subset(data_format, stroke == 0)
avc1 <- subset(data_format, stroke == 1)
# Plot hist age avc = 0
ggplot(avc0, aes(x=age)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(0, 85, by = 5) )+
geom_density(col = 2) +
ggtitle("Histogramme des âges des non AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist age avc = 1
ggplot(avc1, aes(x=age)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(0, 85, by = 5) )+
geom_density(col = 2) +
ggtitle("Histogramme des âges des AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist et densité age avc
ggplot(data_format, aes(x=age, color =stroke, fill = stroke)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2, breaks=seq(0, 85, by = 5) )+
geom_density(alpha=0.2) +
ggtitle("Histogramme des âges en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# boxPlot age
ggplot(data_format, aes(x=stroke, y=age, color=stroke)) +
geom_boxplot() +
ggtitle("Boxplot des âges en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# Regroupement selon plusieurs groupes d'âges
#table(data_format$stroke, data_format$age)
data_format$ageQ <- cut(data_format$age,
breaks = c(-Inf
,29,49,64
, Inf),
labels = c("inf_30"
,"31_50","51_65","65_inf"
),
right = FALSE)
stroke_age <- as.data.frame(table(data_format$stroke, data_format$ageQ))
stroke_age
# conversion de ageQ chr en num
data_format$ageQ <- suppressWarnings(as.factor(data_format$ageQ))
# dataframe de classe d'age en fonction d'avc
ageQ <- as.data.frame(table(data_format$ageQ, data_format$stroke))
colnames(ageQ) <- c('Classe_age','AVC','Fréquence')
# calcul pourcentage avc par classe d'age
ageQ_inf_30_avc <- (ageQ$Fréquence[ageQ$Classe_age =="inf_30" & ageQ$AVC == "avcP"] / sum(ageQ$Fréquence[ageQ$Classe_age =="inf_30"]))*100
ageQ_31_50_avc <- (ageQ$Fréquence[ageQ$Classe_age =="31_50" & ageQ$AVC == "avcP"] / sum(ageQ$Fréquence[ageQ$Classe_age =="31_50"]))*100
ageQ_51_65_avc <- (ageQ$Fréquence[ageQ$Classe_age =="51_65" & ageQ$AVC == "avcP"] / sum(ageQ$Fréquence[ageQ$Classe_age =="51_65"]))*100
ageQ_65_inf_avc <- (ageQ$Fréquence[ageQ$Classe_age =="65_inf" & ageQ$AVC == "avcP"] / sum(ageQ$Fréquence[ageQ$Classe_age =="65_inf"]))*100
print(paste("Pourcentage de inf_30 AVC : " ,round(ageQ_inf_30_avc,2),"%"))
[1] "Pourcentage de inf_30 AVC : 0 %"
print(paste("Pourcentage de 31_50 AVC : " ,round(ageQ_31_50_avc,2),"%"))
[1] "Pourcentage de 31_50 AVC : 1.15 %"
print(paste("Pourcentage de 51_65 AVC : " ,round(ageQ_51_65_avc,2),"%"))
[1] "Pourcentage de 51_65 AVC : 5.29 %"
print(paste("Pourcentage de 65_inf AVC : " ,round(ageQ_65_inf_avc,2),"%"))
[1] "Pourcentage de 65_inf AVC : 14.25 %"
# plot hist par classe d'age
ggplot(ageQ, aes(x = Classe_age, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Classe d'age en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Genre
# dataframe de genre en fonction d'avc
gender <- as.data.frame(table(data_format$gender, data_format$stroke))
colnames(gender) <- c('Genre','AVC','Fréquence')
head(gender)
# calcul pourcentage avc par genre
femme_avc <- (gender$Fréquence[gender$Genre =="Female" & gender$AVC == "avcP"] / sum(gender$Fréquence[gender$Genre =="Female"]))*100
homme_avc <- (gender$Fréquence[gender$Genre =="Male" & gender$AVC == "avcP"] / sum(gender$Fréquence[gender$Genre =="Male"]))*100
print(paste("Pourcentage de femme AVC : " ,round(femme_avc,2),"%"))
[1] "Pourcentage de femme AVC : 5.03 %"
print(paste("Pourcentage de homme AVC : " ,round(homme_avc,2),"%"))
[1] "Pourcentage de homme AVC : 5.6 %"
# plot hist genre
ggplot(gender, aes(x = Genre, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Genre en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Hypertension
# dataframe de hypertension en fonction d'avc
hypertension <- as.data.frame(table(data_format$hypertension, data_format$stroke))
colnames(hypertension) <- c('Hypertension','AVC','Fréquence')
head(hypertension)
# calcul pourcentage avc par hypertension
hypertention0_avc <- (hypertension$Fréquence[hypertension$Hypertension == "htN" & hypertension$AVC == "avcP"] / sum(hypertension$Fréquence[hypertension$Hypertension == "htN"]))*100
hypertention1_avc <- (hypertension$Fréquence[hypertension$Hypertension == "htP" & hypertension$AVC == "avcP"] / sum(hypertension$Fréquence[hypertension$Hypertension == "htP"]))*100
print(paste("Pourcentage de hypertension 0 AVC : " ,round(hypertention0_avc,2),"%"))
[1] "Pourcentage de hypertension 0 AVC : 4.08 %"
print(paste("Pourcentage de hypertension 1 AVC : " ,round(hypertention1_avc,2),"%"))
[1] "Pourcentage de hypertension 1 AVC : 13.97 %"
# plot hist hypertension
ggplot(hypertension, aes(x = Hypertension, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Hypertension en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Maladie cardiaque
# dataframe de Maladie cardiaque en fonction d'avc
maladie_cardiaque <- as.data.frame(table(data_format$heart_disease, data_format$stroke))
colnames(maladie_cardiaque) <- c('Maladie_cardiaque','AVC','Fréquence')
head(maladie_cardiaque)
# calcul pourcentage avc par Maladie cardiaque
maladie_cardiaque0 <- (maladie_cardiaque$Fréquence[maladie_cardiaque$Maladie_cardiaque == "hdN" & maladie_cardiaque$AVC == "avcP"] / sum(maladie_cardiaque$Fréquence[maladie_cardiaque$Maladie_cardiaque == "hdN"]))*100
maladie_cardiaque1 <- (maladie_cardiaque$Fréquence[maladie_cardiaque$Maladie_cardiaque == "hdP" & maladie_cardiaque$AVC == "avcP"] / sum(maladie_cardiaque$Fréquence[maladie_cardiaque$Maladie_cardiaque == "hdP"]))*100
print(paste("Pourcentage de maladie cardiaque 0 AVC : " ,round(maladie_cardiaque0,2),"%"))
[1] "Pourcentage de maladie cardiaque 0 AVC : 4.47 %"
print(paste("Pourcentage de maladie cardiaque 1 AVC : " ,round(maladie_cardiaque1,2),"%"))
[1] "Pourcentage de maladie cardiaque 1 AVC : 17.48 %"
# plot hist Maladie cardiaque
ggplot(maladie_cardiaque, aes(x = Maladie_cardiaque, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Maladie cardiaque en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Mariage
# dataframe de Mariage en fonction d'avc
mariage <- as.data.frame(table(data_format$ever_married, data_format$stroke))
colnames(mariage) <- c('Mariage','AVC','Fréquence')
head(mariage)
# calcul pourcentage avc par Mariage
mariage_no <- (mariage$Fréquence[mariage$Mariage =="No" & mariage$AVC == "avcP"] / sum(mariage$Fréquence[mariage$Mariage =="No"]))*100
mariage_yes <- (mariage$Fréquence[mariage$Mariage =="Yes" & mariage$AVC == "avcP"] / sum(mariage$Fréquence[mariage$Mariage =="Yes"]))*100
print(paste("Pourcentage de pas marié AVC : " ,round(mariage_no,2),"%"))
[1] "Pourcentage de pas marié AVC : 2.42 %"
print(paste("Pourcentage de marié AVC : " ,round(mariage_yes,2),"%"))
[1] "Pourcentage de marié AVC : 6.16 %"
# plot hist Mariage
ggplot(mariage, aes(x = Mariage, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Ayant eu un mariage en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Travail
# dataframe de Travail en fonction d'avc
type_travail <- as.data.frame(table(data_format$work_type, data_format$stroke))
colnames(type_travail) <- c('Type_travail','AVC','Fréquence')
head(type_travail)
# calcul pourcentage avc par Travail
gouvernement_job_avc <- (type_travail$Fréquence[type_travail$Type_travail =="govt_job" & type_travail$AVC == "avcP"] / sum(type_travail$Fréquence[type_travail$Type_travail =="govt_job"]))*100
pas_travail_avc <- (type_travail$Fréquence[type_travail$Type_travail =="never_worked" & type_travail$AVC == "avcP"] / sum(type_travail$Fréquence[type_travail$Type_travail =="never_worked" ]))*100
prive_avc <- (type_travail$Fréquence[type_travail$Type_travail =="private" & type_travail$AVC == "avcP"] / sum(type_travail$Fréquence[type_travail$Type_travail =="private"]))*100
independant_avc <- (type_travail$Fréquence[type_travail$Type_travail =="self_employed" & type_travail$AVC == "avcP"] / sum(type_travail$Fréquence[type_travail$Type_travail =="self_employed"]))*100
print(paste("Pourcentage de job gouvernement AVC : " ,round(gouvernement_job_avc,2),"%"))
[1] "Pourcentage de job gouvernement AVC : 0 %"
print(paste("Pourcentage de pas de travail AVC : " ,round(pas_travail_avc,2),"%"))
[1] "Pourcentage de pas de travail AVC : 4.47 %"
print(paste("Pourcentage de job privé AVC : " ,round(prive_avc,2),"%"))
[1] "Pourcentage de job privé AVC : 4.95 %"
print(paste("Pourcentage de indépendant AVC : " ,round(independant_avc,2),"%"))
[1] "Pourcentage de indépendant AVC : 7.63 %"
# plot hist Travail
ggplot(type_travail, aes(x = Type_travail, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Type de travail en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Résidence
# dataframe de Résidence en fonction d'avc
type_residence <- as.data.frame(table(data_format$Residence_type, data_format$stroke))
colnames(type_residence) <- c('Type_residence','AVC','Fréquence')
head(type_residence)
# calcul pourcentage avc par Résidence
rural_avc <- (type_residence$Fréquence[type_residence$Type_residence =="Rural" & type_residence$AVC == "avcP"] / sum(type_residence$Fréquence[type_residence$Type_residence =="Rural"]))*100
urbain_avc <- (type_residence$Fréquence[type_residence$Type_residence =="Urban" & type_residence$AVC == "avcP"] / sum(type_residence$Fréquence[type_residence$Type_residence =="Urban"]))*100
print(paste("Pourcentage de rural AVC : " ,round(rural_avc,2),"%"))
[1] "Pourcentage de rural AVC : 5.12 %"
print(paste("Pourcentage de urbain AVC : " ,round(urbain_avc),"%"))
[1] "Pourcentage de urbain AVC : 5 %"
# plot hist Résidence
ggplot(type_residence, aes(x = Type_residence, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Type de résidence en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Niveau glucose
min(avc0$avg_glucose_level)
Warning in min(avc0$avg_glucose_level) :
no non-missing arguments to min; returning Inf
[1] Inf
max(avc0$avg_glucose_level)
Warning in max(avc0$avg_glucose_level) :
no non-missing arguments to max; returning -Inf
[1] -Inf
min(avc1$avg_glucose_level)
Warning in min(avc1$avg_glucose_level) :
no non-missing arguments to min; returning Inf
[1] Inf
max(avc1$avg_glucose_level)
Warning in max(avc1$avg_glucose_level) :
no non-missing arguments to max; returning -Inf
[1] -Inf
# Plot hist avg_glucose_level avc = 0
ggplot(avc0, aes(x=avg_glucose_level)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(55, 275, by = 10) )+
geom_density(col = 2) +
ggtitle("Histogramme des niveaux de glucose des non AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist avg_glucose_level avc = 1
ggplot(avc1, aes(x=avg_glucose_level)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(55, 275, by = 10) )+
geom_density(col = 2) +
ggtitle("Histogramme des niveaux de glucose des AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist et densité avg_glucose_level avc
ggplot(data_format, aes(x=avg_glucose_level, color =stroke, fill = stroke)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2, breaks=seq(55, 275, by = 10) )+
geom_density(alpha=0.2) +
ggtitle("Histogramme des niveaux de glucose en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# boxPlot avg_glucose_level
ggplot(data_format, aes(x=stroke, y=avg_glucose_level, color=stroke)) +
geom_boxplot() +
ggtitle("Boxplot des niveaux de glucose en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# Regroupement selon les niveaux de glucose
#summary(data_format$avg_glucose_level)
data_format$glucoseQ <- cut(data_format$avg_glucose_level,
breaks = c(-Inf
,69, 99,125
, Inf),
labels = c("inf_69", "70_100"
,"101_126","127_inf"
),
right = FALSE)
stroke_glc <- as.data.frame(table(data_format$stroke, data_format$glucoseQ))
stroke_glc
# conversion de glucoseQ chr en num
data_format$glucoseQ <- suppressWarnings(as.factor(data_format$glucoseQ))
# dataframe de classe glucose en fonction d'avc
glucoseQ <- as.data.frame(table(data_format$glucoseQ, data_format$stroke))
colnames(glucoseQ) <- c('Classe_glucose','AVC','Fréquence')
# calcul pourcentage avc par classe de glucose
glucoseQ_inf_69_avc <- (glucoseQ$Fréquence[glucoseQ$Classe_glucose =="inf_69" & glucoseQ$AVC == "avcP"] / sum(glucoseQ$Fréquence[glucoseQ$Classe_glucose =="inf_69"]))*100
glucoseQ_70_100_avc <- (glucoseQ$Fréquence[glucoseQ$Classe_glucose =="70_100" & glucoseQ$AVC == "avcP"] / sum(glucoseQ$Fréquence[glucoseQ$Classe_glucose =="70_100"]))*100
glucoseQ_101_126_avc <- (glucoseQ$Fréquence[glucoseQ$Classe_glucose =="101_126" & glucoseQ$AVC == "avcP"] / sum(glucoseQ$Fréquence[glucoseQ$Classe_glucose =="101_126"]))*100
glucoseQ_127_inf_avc <- (glucoseQ$Fréquence[glucoseQ$Classe_glucose =="127_inf" & glucoseQ$AVC == "avcP"] / sum(glucoseQ$Fréquence[glucoseQ$Classe_glucose =="127_inf"]))*100
print(paste("Pourcentage de inf_69 AVC : " ,round(glucoseQ_inf_69_avc,2),"%"))
[1] "Pourcentage de inf_69 AVC : 3.5 %"
print(paste("Pourcentage de 70_100 AVC : " ,round(glucoseQ_70_100_avc,2),"%"))
[1] "Pourcentage de 70_100 AVC : 3.85 %"
print(paste("Pourcentage de 101_126 AVC : " ,round(glucoseQ_101_126_avc,2),"%"))
[1] "Pourcentage de 101_126 AVC : 3.94 %"
print(paste("Pourcentage de 127_inf AVC : " ,round(glucoseQ_127_inf_avc,2),"%"))
[1] "Pourcentage de 127_inf AVC : 10.62 %"
# plot hist par classe de glucose
ggplot(glucoseQ, aes(x = Classe_glucose, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Classe de niveau de glucose en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - IMC
min(avc0$bmi)
Warning in min(avc0$bmi) :
no non-missing arguments to min; returning Inf
[1] Inf
max(avc0$bmi)
Warning in max(avc0$bmi) :
no non-missing arguments to max; returning -Inf
[1] -Inf
min(avc1$bmi)
Warning in min(avc1$bmi) :
no non-missing arguments to min; returning Inf
[1] Inf
max(avc1$bmi)
Warning in max(avc1$bmi) :
no non-missing arguments to max; returning -Inf
[1] -Inf
# Plot hist IMC avc = 0
ggplot(avc0, aes(x=bmi)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(10, 100, by = 5) )+
geom_density(col = 2) +
ggtitle("Histogramme des IMC des non AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist IMC avc = 1
ggplot(avc1, aes(x=bmi)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2,col="#00AFBB", fill="#00AFBB", breaks=seq(10, 100, by = 5) )+
geom_density(col = 2) +
ggtitle("Histogramme des IMC des AVCs") + theme(plot.title = element_text(hjust = 0.5))
# Plot hist et densité IMC avc
ggplot(data_format, aes(x=bmi, color =stroke, fill = stroke)) +
geom_histogram(aes(y =..density..),position="identity", alpha=0.2, breaks=seq(10, 100, by = 5) )+
geom_density(alpha=0.2) +
ggtitle("Histogramme des IMC en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# boxPlot IMC
ggplot(data_format, aes(x=stroke, y=bmi, color=stroke)) +
geom_boxplot() +
ggtitle("Boxplot des IMC en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# Regroupement selon les groupes d'IMC
#summary(data_format$bmi)
data_format$bmiQ <- cut(data_format$bmi,
breaks = c(-Inf
,24.9, 29.9, 34.9
, Inf),
labels = c("inf_25"
,"25_30","30_35",
"35_inf"
),
right = FALSE)
stroke_bmi <- as.data.frame(table(data_format$stroke, data_format$bmiQ))
#stroke_bmi
# conversion de bmiQ chr en num
data_format$bmiQ <- suppressWarnings(as.factor(data_format$bmiQ))
#data_format$bmiQ
# dataframe de classe imc en fonction d'avc
bmiQ <- as.data.frame(table(data_format$bmiQ, data_format$stroke))
colnames(bmiQ) <- c('Classe_IMC','AVC','Fréquence')
head(bmiQ)
# calcul pourcentage avc par classe d'imc
bmiQ_inf_25_avc <- (bmiQ$Fréquence[bmiQ$Classe_IMC =="inf_25" & bmiQ$AVC == "avcP"] / sum(bmiQ$Fréquence[bmiQ$Classe_IMC =="inf_25"]))*100
bmiQ_25_30_avc <- (bmiQ$Fréquence[bmiQ$Classe_IMC =="25_30" & bmiQ$AVC == "avcP"] / sum(bmiQ$Fréquence[bmiQ$Classe_IMC =="25_30"]))*100
bmiQ_30_35_avc <- (bmiQ$Fréquence[bmiQ$Classe_IMC =="30_35" & bmiQ$AVC == "avcP"] / sum(bmiQ$Fréquence[bmiQ$Classe_IMC =="30_35"]))*100
bmiQ_35_inf_avc <- (bmiQ$Fréquence[bmiQ$Classe_IMC =="35_inf" & bmiQ$AVC == "avcP"] / sum(bmiQ$Fréquence[bmiQ$Classe_IMC =="35_inf"]))*100
print(paste("Pourcentage de inf_25 AVC : " ,round(bmiQ_inf_25_avc,2),"%"))
[1] "Pourcentage de inf_25 AVC : 3.87 %"
print(paste("Pourcentage de 25_30 AVC : " ,round(bmiQ_25_30_avc,2),"%"))
[1] "Pourcentage de 25_30 AVC : 5.59 %"
print(paste("Pourcentage de 30_35 AVC : " ,round(bmiQ_30_35_avc,2),"%"))
[1] "Pourcentage de 30_35 AVC : 6.38 %"
print(paste("Pourcentage de 35_inf AVC : " ,round(bmiQ_35_inf_avc,2),"%"))
[1] "Pourcentage de 35_inf AVC : 5.01 %"
# plot hist par classe d'imc
ggplot(bmiQ, aes(x = Classe_IMC, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Classe de niveau d'IMC en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - Fumeur
# dataframe de Fumeur en fonction d'avc
fumeur <- as.data.frame(table(data_format$smoking_status, data_format$stroke))
colnames(fumeur) <- c('Statut_fumeur','AVC','Fréquence')
head(fumeur)
# calcul pourcentage avc par Fumeur
ancien_fumeur_avc <- (fumeur$Fréquence[fumeur$Statut_fumeur =="formerly" & fumeur$AVC == "avcP"] / sum(fumeur$Fréquence[fumeur$Statut_fumeur =="formerly"]))*100
non_fumeur_avc <- (fumeur$Fréquence[fumeur$Statut_fumeur =="never" & fumeur$AVC == "avcP"] / sum(fumeur$Fréquence[fumeur$Statut_fumeur =="never"]))*100
fumeur_avc <- (fumeur$Fréquence[fumeur$Statut_fumeur =="smokes" & fumeur$AVC == "avcP"] / sum(fumeur$Fréquence[fumeur$Statut_fumeur =="smokes"]))*100
print(paste("Pourcentage de ancien fumeur AVC : " ,round(ancien_fumeur_avc,2),"%"))
[1] "Pourcentage de ancien fumeur AVC : 6.82 %"
print(paste("Pourcentage de non fumeur AVC : " ,round(non_fumeur_avc,2),"%"))
[1] "Pourcentage de non fumeur AVC : 4.54 %"
print(paste("Pourcentage de fumeur AVC : " ,round(fumeur_avc,2),"%"))
[1] "Pourcentage de fumeur AVC : 5.29 %"
# plot hist Fumeur
ggplot(fumeur, aes(x = Statut_fumeur, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Statut fumeur en fonction des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))+
geom_text(aes(label = Fréquence), vjust = 0)
# Variable - AVC
# dataframe de AVC
avc <- as.data.frame(table( data_format$stroke))
colnames(avc) <- c('AVC','Fréquence')
head(avc)
# plot hist genre
ggplot(avc, aes(x = AVC, y = Fréquence, fill = AVC)) +
geom_bar(stat = "identity") +
ggtitle("Nombre d'AVC du jeu de donnée") + theme(plot.title = element_text(hjust = 0.5), legend.position="none")+
geom_text(aes(label = Fréquence), vjust = 0)
# garde attr quanti
data_cor<- select(data_format,age,avg_glucose_level,bmi)
# calcul corrélation
M<-cor(data_cor)
# plot corrélation
corrplot(M, method="circle")
corrplot(M, method="color")
corrplot(M, method="number")
# plot IMC en fonction de age
ggplot(data_format, aes(x = age, y = bmi, colour = stroke)) +
geom_point(alpha=0.5)+
ggtitle("IMC en fonction de l'âge et des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
# plot niveau de glucose en fonction de age
ggplot(data_format, aes(x = age, y = avg_glucose_level, colour = stroke)) +
geom_point(alpha=0.5)+
ggtitle("Niveau de glucose en fonction de l'âge et des chances de faire un AVC") + theme(plot.title = element_text(hjust = 0.5))
Entre AVC et les variables quantitatives.
# avec age
var.test(age~stroke,data=data_format) # sous reserve d'hypothese de normalite des distributions des ages selon absence/presence d'AVC
F test to compare two variances
data: age by stroke
F = 2.4437, num df = 3244, denom df = 179, p-value = 0.000000000000552
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
1.953901 2.993932
sample estimates:
ratio of variances
2.443703
t.test(age~stroke,var.equal=T,data=data_format)
Two Sample t-test
data: age by stroke
t = -14.622, df = 3423, p-value < 0.00000000000000022
alternative hypothesis: true difference in means between group avcN and group avcP is not equal to 0
95 percent confidence interval:
-23.22533 -17.73324
sample estimates:
mean in group avcN mean in group avcP
47.57627 68.05556
# difference signifcative ### => le risque augmente avec l'age
# avec NivGluc
var.test(avg_glucose_level~stroke,data=data_format)
F test to compare two variances
data: avg_glucose_level by stroke
F = 0.53379, num df = 3244, denom df = 179, p-value = 0.0000000001614
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
0.4268002 0.6539792
sample estimates:
ratio of variances
0.53379
t.test(avg_glucose_level~stroke,var.equal=T,data=data_format)
Two Sample t-test
data: avg_glucose_level by stroke
t = -8.3034, df = 3423, p-value < 0.00000000000000022
alternative hypothesis: true difference in means between group avcN and group avcP is not equal to 0
95 percent confidence interval:
-37.12956 -22.94450
sample estimates:
mean in group avcN mean in group avcP
106.7331 136.7701
# difference signifcative #### => le risque augmente avec le niveau de glucose
# avec IMC
var.test(bmi~stroke,data=data_format)
F test to compare two variances
data: bmi by stroke
F = 1.3545, num df = 3244, denom df = 179, p-value = 0.008306
alternative hypothesis: true ratio of variances is not equal to 1
95 percent confidence interval:
1.083041 1.659526
sample estimates:
ratio of variances
1.354536
t.test(bmi~stroke,var.equal=T,data=data_format)
Two Sample t-test
data: bmi by stroke
t = -0.67878, df = 3423, p-value = 0.4973
alternative hypothesis: true difference in means between group avcN and group avcP is not equal to 0
95 percent confidence interval:
-1.4747016 0.7162065
sample estimates:
mean in group avcN mean in group avcP
30.27242 30.65167
# difference non signifcative (discutable) => le risque n'augmente pas significativement avec l'IMC
Entre stroke et les variables qualitatives.
# Genre
t <- chisq.test(data_format$gender,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals #===> les hommes ont plus de chance de faire un AVC que les femmes
data_format$stroke
data_format$gender avcN avcP
Female 0.1041289 -0.4421224
Male -0.1299686 0.5518352
# Maladie cardiaque
t <- chisq.test(data_format$heart_disease,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # les maladies cardiaques ont plus de chance d'etre associees avec la survenue d'un AVC
data_format$stroke
data_format$heart_disease avcN avcP
hdN 0.4558373 -1.9354457
hdP -1.8019252 7.6508179
# Mariage
t <- chisq.test(data_format$ever_married,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # => les personnes ayant deja ete mariees ont plus de chance de faire un AVC
data_format$stroke
data_format$ever_married avcN avcP
No 0.8368322 -3.5531168
Yes -0.4717645 2.0030711
# Travail
t <- chisq.test(data_format$work_type,data_format$stroke) #rejet de l'hypothese d'independance
Warning in chisq.test(data_format$work_type, data_format$stroke) :
Chi-squared approximation may be incorrect
t$residuals # => les personnes self_employed ont plus de chance de faire un AVC
data_format$stroke
data_format$work_type avcN avcP
never_worked 0.1818551 -0.7721411
govt_job 0.4889245 -2.0759309
private 0.1450101 -0.6157004
self_employed -0.6121211 2.5990128
# Residence
t <- chisq.test(data_format$Residence_type,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals #==> les personnes vivant en milieu urbain ont plus de chance de faire un AVC
data_format$stroke
data_format$Residence_type avcN avcP
Rural 0.05744833 -0.24392060
Urban -0.05636822 0.23933455
# Fumeur
t <- chisq.test(data_format$smoking_status,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # les personnes ayant deja fume ont plus de chance de faire un AVC
data_format$stroke
data_format$smoking_status avcN avcP
formerly -0.46419850 1.97094649
never 0.31825686 -1.35129097
smokes -0.01010997 0.04292604
# age
t <- chisq.test(data_format$ageQ,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # les personnes de plus de 65 ans ont plus de chance de faire un AVC
data_format$stroke
data_format$ageQ avcN avcP
inf_30 1.33242727 -5.65737036
31_50 1.36198605 -5.78287437
51_65 -0.01122133 0.04764479
65_inf -2.65934496 11.29134751
# glucose
t <- chisq.test(data_format$glucoseQ,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # les personnes ayant un niveau de glucose supérieur a 127 ont plus de chance de faire un AVC
data_format$stroke
data_format$glucoseQ avcN avcP
inf_69 0.3853051 -1.6359721
70_100 0.5683127 -2.4130062
101_126 0.3550848 -1.5076590
127_inf -1.4841544 6.3015906
# bmi
t <- chisq.test(data_format$bmiQ,data_format$stroke) #rejet de l'hypothese d'independance
t$residuals # les personnes ayant un imc entre 30 et 35 ont plus de chance de faire un AVC
data_format$stroke
data_format$bmiQ avcN avcP
inf_25 0.39765899 -1.68842552
25_30 -0.11392542 0.48371744
30_35 -0.32742703 1.39022669
35_inf 0.07044428 -0.29910030
print(paste("----------------------------------------------Genre------------------------------------------------- " ))
[1] "----------------------------------------------Genre------------------------------------------------- "
print(paste("Pourcentage de femme AVC : " ,round(femme_avc,2),"%"))
[1] "Pourcentage de femme AVC : 5.03 %"
print(paste("Pourcentage de homme AVC : " ,round(homme_avc,2),"%"))
[1] "Pourcentage de homme AVC : 5.6 %"
print(paste("----------------------------------------------Hypertension------------------------------------------------- " ))
[1] "----------------------------------------------Hypertension------------------------------------------------- "
print(paste("Pourcentage de hypertension AVC : " ,round(hypertention0_avc,2),"%"))
[1] "Pourcentage de hypertension AVC : 4.08 %"
print(paste("Pourcentage de hypertension AVC : " ,round(hypertention1_avc,2),"%"))
[1] "Pourcentage de hypertension AVC : 13.97 %"
print(paste("----------------------------------------------Maladie cardiaque------------------------------------------------- " ))
[1] "----------------------------------------------Maladie cardiaque------------------------------------------------- "
print(paste("Pourcentage de maladie cardiaque AVC : " ,round(maladie_cardiaque0,2),"%"))
[1] "Pourcentage de maladie cardiaque AVC : 4.47 %"
print(paste("Pourcentage de maladie cardiaque AVC : " ,round(maladie_cardiaque1,2),"%"))
[1] "Pourcentage de maladie cardiaque AVC : 17.48 %"
print(paste("----------------------------------------------Mariage------------------------------------------------- " ))
[1] "----------------------------------------------Mariage------------------------------------------------- "
print(paste("Pourcentage de pas marié AVC : " ,round(mariage_no,2),"%"))
[1] "Pourcentage de pas marié AVC : 2.42 %"
print(paste("Pourcentage de marié AVC : " ,round(mariage_yes,2),"%"))
[1] "Pourcentage de marié AVC : 6.16 %"
print(paste("----------------------------------------------Type Job------------------------------------------------- " ))
[1] "----------------------------------------------Type Job------------------------------------------------- "
print(paste("Pourcentage de job gouvernement AVC : " ,round(gouvernement_job_avc,2),"%"))
[1] "Pourcentage de job gouvernement AVC : 0 %"
print(paste("Pourcentage de pas de travail AVC : " ,round(pas_travail_avc,2),"%"))
[1] "Pourcentage de pas de travail AVC : 4.47 %"
print(paste("Pourcentage de job privé AVC : " ,round(prive_avc,2),"%"))
[1] "Pourcentage de job privé AVC : 4.95 %"
print(paste("Pourcentage de indépendant AVC : " ,round(independant_avc,2),"%"))
[1] "Pourcentage de indépendant AVC : 7.63 %"
print(paste("----------------------------------------------Type résidence------------------------------------------------- " ))
[1] "----------------------------------------------Type résidence------------------------------------------------- "
print(paste("Pourcentage de rural AVC : " ,round(rural_avc,2),"%"))
[1] "Pourcentage de rural AVC : 5.12 %"
print(paste("Pourcentage de urbain AVC : " ,round(urbain_avc),"%"))
[1] "Pourcentage de urbain AVC : 5 %"
print(paste("----------------------------------------------Status fumeur------------------------------------------------- " ))
[1] "----------------------------------------------Status fumeur------------------------------------------------- "
print(paste("Pourcentage de ancien fumeur AVC : " ,round(ancien_fumeur_avc,2),"%"))
[1] "Pourcentage de ancien fumeur AVC : 6.82 %"
print(paste("Pourcentage de non fumeur AVC : " ,round(non_fumeur_avc,2),"%"))
[1] "Pourcentage de non fumeur AVC : 4.54 %"
print(paste("Pourcentage de fumeur AVC : " ,round(fumeur_avc,2),"%"))
[1] "Pourcentage de fumeur AVC : 5.29 %"
print(paste("----------------------------------------------Classe d'age------------------------------------------------- " ))
[1] "----------------------------------------------Classe d'age------------------------------------------------- "
print(paste("Pourcentage de inf_30 AVC : " ,round(ageQ_inf_30_avc,2),"%"))
[1] "Pourcentage de inf_30 AVC : 0 %"
print(paste("Pourcentage de 31_50 AVC : " ,round(ageQ_31_50_avc,2),"%"))
[1] "Pourcentage de 31_50 AVC : 1.15 %"
print(paste("Pourcentage de 51_65 AVC : " ,round(ageQ_51_65_avc,2),"%"))
[1] "Pourcentage de 51_65 AVC : 5.29 %"
print(paste("Pourcentage de 65_inf AVC : " ,round(ageQ_65_inf_avc,2),"%"))
[1] "Pourcentage de 65_inf AVC : 14.25 %"
print(paste("----------------------------------------------Classe de glucose------------------------------------------------- " ))
[1] "----------------------------------------------Classe de glucose------------------------------------------------- "
print(paste("Pourcentage de inf_69 AVC : " ,round(glucoseQ_inf_69_avc,2),"%"))
[1] "Pourcentage de inf_69 AVC : 3.5 %"
print(paste("Pourcentage de 70_100 AVC : " ,round(glucoseQ_70_100_avc,2),"%"))
[1] "Pourcentage de 70_100 AVC : 3.85 %"
print(paste("Pourcentage de 101_126 AVC : " ,round(glucoseQ_101_126_avc,2),"%"))
[1] "Pourcentage de 101_126 AVC : 3.94 %"
print(paste("Pourcentage de 127_inf AVC : " ,round(glucoseQ_127_inf_avc,2),"%"))
[1] "Pourcentage de 127_inf AVC : 10.62 %"
print(paste("----------------------------------------------Classe d'IMC------------------------------------------------- " ))
[1] "----------------------------------------------Classe d'IMC------------------------------------------------- "
print(paste("Pourcentage de inf_25 AVC : " ,round(bmiQ_inf_25_avc,2),"%"))
[1] "Pourcentage de inf_25 AVC : 3.87 %"
print(paste("Pourcentage de 25_30 AVC : " ,round(bmiQ_25_30_avc,2),"%"))
[1] "Pourcentage de 25_30 AVC : 5.59 %"
print(paste("Pourcentage de 30_35 AVC : " ,round(bmiQ_30_35_avc,2),"%"))
[1] "Pourcentage de 30_35 AVC : 6.38 %"
print(paste("Pourcentage de 35_inf AVC : " ,round(bmiQ_35_inf_avc,2),"%"))
[1] "Pourcentage de 35_inf AVC : 5.01 %"
# selection des seules variables qualitatives pour l'AFCM
avcQ = data_format[,-c(2,8,9)]
summary(avcQ)
gender hypertension heart_disease ever_married work_type Residence_type smoking_status stroke ageQ glucoseQ bmiQ
Female:2086 htN:3017 hdN:3219 No : 826 never_worked : 514 Rural:1680 formerly: 836 avcN:3245 inf_30: 609 inf_69 : 457 inf_25: 776
Male :1339 htP: 408 hdP: 206 Yes:2599 govt_job : 82 Urban:1745 never :1852 avcP: 180 31_50 :1043 70_100 :1557 25_30 :1091
private :2200 smokes : 737 51_65 : 945 101_126: 686 30_35 : 799
self_employed: 629 65_inf: 828 127_inf: 725 35_inf: 759
quali <- avcQ[,-8]
var_sup <- avcQ[,c("stroke")]
acm <- dudi.acm(quali, scannf = FALSE, nf = 5)
acm$supv <- supcol(acm, dudi.acm(var_sup, scannf = FALSE, nf = 5)$tab)
#explor(acm)
Creation d’une variable motivée par l’étude exploratoire descriptive
# creation de la variable interaction entre age et hypertension
AgeHypertension=avcQ$ageQ:avcQ$hypertension
table(AgeHypertension,avcQ[,"stroke"])
AgeHypertension avcN avcP
inf_30:htN 600 0
inf_30:htP 9 0
31_50:htN 984 11
31_50:htP 47 1
51_65:htN 755 36
51_65:htP 140 14
65_inf:htN 555 76
65_inf:htP 155 42
# test de dependence avec la variable cible
t <- chisq.test(AgeHypertension,avcQ$stroke)
Warning in chisq.test(AgeHypertension, avcQ$stroke) :
Chi-squared approximation may be incorrect
t$residuals
avcQ$stroke
AgeHypertension avcN avcP
inf_30:htN 1.3225451 -5.6154115
inf_30:htP 0.1619780 -0.6877446
31_50:htN 1.3448598 -5.7101578
31_50:htP 0.2257855 -0.9586656
51_65:htN 0.2034944 -0.8640196
51_65:htP -0.4889876 2.0761990
65_inf:htN -1.7520117 7.4388893
65_inf:htP -2.3164262 9.8353442
#avcQ2=data.frame(avcQ,"AgeHypertension"=AgeHypertension)
#summary(avcQ2)
#quali2 <- avcQ2[,-c(1,9,10)]
#var_sup2 <- avcQ2[,c("ageQ","stroke","hypertension")]
#acm2 <- dudi.acm(quali2, scannf = FALSE, nf = 5)
#acm2$supv <- supcol(acm2, dudi.acm(var_sup2, scannf = FALSE, nf = 5)$tab)
#explor(acm2)
imbalanceRatio(as.data.frame(data_format), classAttr = "stroke")
[1] 0.05546995
stroke_test <- data_format %>%
mutate(
stroke = as.character(stroke),
across(where(is.factor), as.numeric),
stroke = factor(stroke)
)
stroke_oversampled <- oversample(as.data.frame(stroke_test), classAttr = "stroke", ratio = 1, method = "MWMOTE")
str(stroke_oversampled)
'data.frame': 6490 obs. of 14 variables:
$ gender : num 2 2 1 1 2 2 1 1 1 1 ...
$ age : num 67 80 49 79 81 74 69 81 61 54 ...
$ hypertension : num 1 1 1 2 1 2 1 2 1 1 ...
$ heart_disease : num 2 2 1 1 1 2 1 1 2 1 ...
$ ever_married : num 2 2 2 2 2 2 1 2 2 2 ...
$ work_type : num 3 3 3 4 3 3 3 3 1 3 ...
$ Residence_type : num 2 1 2 1 2 1 2 1 1 2 ...
$ avg_glucose_level: num 229 106 171 174 186 ...
$ bmi : num 36.6 32.5 34.4 24 29 27.4 22.8 29.7 36.8 27.3 ...
$ smoking_status : num 1 2 3 2 1 2 2 2 3 3 ...
$ stroke : Factor w/ 2 levels "avcN","avcP": 2 2 2 2 2 2 2 2 2 2 ...
$ ageQ : num 4 4 3 4 4 4 4 4 3 3 ...
$ glucoseQ : num 4 3 4 4 4 2 2 2 3 3 ...
$ bmiQ : num 4 3 3 1 2 2 1 2 4 2 ...
- attr(*, "na.action")= 'omit' Named int [1:140] 2 24 36 42 45 48 59 67 85 92 ...
..- attr(*, "names")= chr [1:140] "2" "24" "36" "42" ...
stroke_oversampled %>%
group_by(stroke) %>%
summarize(n = n()) %>%
mutate(prop = round(n / sum(n), 2))
for ( i in 1:ncol(data_format)) {
if(is.factor(data_format[,i])){
stroke_oversampled[,i] <- factor(as.integer(stroke_oversampled[,i]),labels=c(levels(data_format[,i])))
}
}
str(stroke_oversampled)
'data.frame': 6490 obs. of 14 variables:
$ gender : Factor w/ 2 levels "Female","Male": 2 2 1 1 2 2 1 1 1 1 ...
$ age : num 67 80 49 79 81 74 69 81 61 54 ...
$ hypertension : Factor w/ 2 levels "htN","htP": 1 1 1 2 1 2 1 2 1 1 ...
$ heart_disease : Factor w/ 2 levels "hdN","hdP": 2 2 1 1 1 2 1 1 2 1 ...
$ ever_married : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 1 2 2 2 ...
$ work_type : Factor w/ 4 levels "never_worked",..: 3 3 3 4 3 3 3 3 1 3 ...
$ Residence_type : Factor w/ 2 levels "Rural","Urban": 2 1 2 1 2 1 2 1 1 2 ...
$ avg_glucose_level: num 229 106 171 174 186 ...
$ bmi : num 36.6 32.5 34.4 24 29 27.4 22.8 29.7 36.8 27.3 ...
$ smoking_status : Factor w/ 3 levels "formerly","never",..: 1 2 3 2 1 2 2 2 3 3 ...
$ stroke : Factor w/ 2 levels "avcN","avcP": 2 2 2 2 2 2 2 2 2 2 ...
$ ageQ : Factor w/ 4 levels "inf_30","31_50",..: 4 4 3 4 4 4 4 4 3 3 ...
$ glucoseQ : Factor w/ 4 levels "inf_69","70_100",..: 4 3 4 4 4 2 2 2 3 3 ...
$ bmiQ : Factor w/ 4 levels "inf_25","25_30",..: 4 3 3 1 2 2 1 2 4 2 ...
- attr(*, "na.action")= 'omit' Named int [1:140] 2 24 36 42 45 48 59 67 85 92 ...
..- attr(*, "names")= chr [1:140] "2" "24" "36" "42" ...
data_format_undersample <- data_format %>%
group_by(stroke) %>%
sample_n(180)
table(data_format_undersample$stroke)
avcN avcP
180 180
set.seed(42)
### APPRENTISSAGE SUPERVISE
# objectif double :
## Prédire efficacement la présence (ou l’absence) d’un AVC (variable ‘stroke’)
## Ã partir de nouvelles observations sur les variables explicatives
## Comprendre les facteurs influençant la présence d’une maladie coronarienne
# dataset avec meme attr de depart non équilibré
data_format_raw <- data_format[-c(12,13,14)]
# dataset avec meme attr de depart équilibré oversample
data_format_oversample_raw <- stroke_oversampled[-c(12,13,14)]
# data avec que variable qualitatif équilibré oversample
data_format_oversample_quali <- stroke_oversampled[-c(2,8,9)]
# dataset avec meme attr de depart équilibré undersample
data_format_undersample_raw <- data_format_undersample[-c(12,13,14)]
# separation echantillons apprentissage / test dataset non équilibré de départ
df_sampling_index <- createDataPartition(data_format_raw$stroke, times = 1, p = 0.7, list = FALSE)
df_training_unbalanced <- data_format_raw[df_sampling_index, ]
df_testing_unbalanced <- data_format_raw[-df_sampling_index, ]
# separation echantillons apprentissage / test dataset équilibré de depart oversample
df_sampling_index <- createDataPartition(data_format_oversample_raw$stroke, times = 1, p = 0.7, list = FALSE)
df_training_over <- data_format_oversample_raw[df_sampling_index, ]
df_testing_over <- data_format_oversample_raw[-df_sampling_index, ]
# separation echantillons apprentissage / test dataset équilibré variable qualitatif oversample
df_sampling_index <- createDataPartition(data_format_oversample_quali$stroke, times = 1, p = 0.7, list = FALSE)
df_training_over_quali <- data_format_oversample_quali[df_sampling_index, ]
df_testing_over_quali <- data_format_oversample_quali[-df_sampling_index, ]
# separation echantillons apprentissage / test dataset équilibré de depart undersample
df_sampling_index <- createDataPartition(data_format_undersample_raw$stroke, times = 1, p = 0.7, list = FALSE)
df_training_under <- data_format_undersample_raw[df_sampling_index, ]
df_testing_under <- data_format_undersample_raw[-df_sampling_index, ]
### PHASE D APRENTISSAGE
## 1) On lance les algorithmes avec les parametres par defaut
## 2) On optimise les parametres de complexite par CV V-5
### on definit le cadre general de l'optimisation des parametres de complexite dans la fonction trainControl (a utiliser dans la fonciton train du package caret)
df_control <- trainControl(method="cv", #validation croisee
number = 5, # 5 folds (selon le nombre de donnees on peut mettre plus)
classProbs = TRUE,
summaryFunction = twoClassSummary) # on est dans un cas de classification binaire
print("SVM")
[1] "SVM"
# apprentissage
model_svm_unbalanced=svm(stroke~., data=df_training_unbalanced, cost=0.5, kernel="linear")
# prediction
prediction_svm_unbalanced <- predict(model_svm_unbalanced, df_testing_unbalanced)
# matrice de confusion
matconfus_svm_unbalanced <- table(prediction_svm_unbalanced, df_testing_unbalanced$stroke)
matconfus_svm_unbalanced
prediction_svm_unbalanced avcN avcP
avcN 973 54
avcP 0 0
# calcul de l'erreur de prediction
err_svm_unbalanced <- 1-(sum(diag(matconfus_svm_unbalanced)) / sum(matconfus_svm_unbalanced))
err_svm_unbalanced*100
[1] 5.258033
# kappa / sensibilité / spécificité
kappa_svm_unbalanced <- CohenKappa(matconfus_svm_unbalanced)
sensitivity_svm_unbalanced <- sensitivity(prediction_svm_unbalanced, df_testing_unbalanced$stroke)
specificity_svm_unbalanced <- specificity(prediction_svm_unbalanced, df_testing_unbalanced$stroke)
print("Random forest")
[1] "Random forest"
# apprentissage
model_rf_unbalanced <- train(stroke ~.,
data = df_training_unbalanced,
method = "rf",
metric = "ROC",
preProcess = c("scale", "center"),
trControl = df_control)
# prediction
prediction_rf_unbalanced <- predict(model_rf_unbalanced, df_testing_unbalanced)
# matrice de confusion
matconfus_rf_unbalanced <- table(prediction_rf_unbalanced, df_testing_unbalanced$stroke)
matconfus_rf_unbalanced
prediction_rf_unbalanced avcN avcP
avcN 966 49
avcP 7 5
# calcul de l'erreur de prediction
err_rf_unbalanced <- 1-(sum(diag(matconfus_rf_unbalanced)) / sum(matconfus_rf_unbalanced))
err_rf_unbalanced*100
[1] 5.452775
# kappa / sensibilité / spécificité
kappa_rf_unbalanced <- CohenKappa(matconfus_rf_unbalanced)
sensitivity_rf_unbalanced <- sensitivity(prediction_rf_unbalanced, df_testing_unbalanced$stroke)
specificity_rf_unbalanced <- specificity(prediction_rf_unbalanced, df_testing_unbalanced$stroke)
print("SVM")
[1] "SVM"
# apprentissage
model_svm_over=svm(stroke~., data=df_training_over, cost=0.5, kernel="linear")
# prediction
prediction_svm_over <- predict(model_svm_over, df_testing_over)
# matrice de confusion
matconfus_svm_over <- table(prediction_svm_over, df_testing_over$stroke)
matconfus_svm_over
prediction_svm_over avcN avcP
avcN 799 148
avcP 174 825
# calcul de l'erreur de prediction
err_svm_over <- 1-(sum(diag(matconfus_svm_over)) / sum(matconfus_svm_over))
err_svm_over*100
[1] 16.54676
# kappa / sensibilité / spécificité
kappa_svm_over <- CohenKappa(matconfus_svm_over)
sensitivity_svm_over <- sensitivity(prediction_svm_over, df_testing_over$stroke)
specificity_svm_over <- specificity(prediction_svm_over, df_testing_over$stroke)
print("Random forest")
[1] "Random forest"
# apprentissage
model_rf_over <- train(stroke ~.,
data = df_training_over,
method = "rf",
metric = "ROC",
preProcess = c("scale", "center"),
trControl = df_control)
# prediction
prediction_rf_over <- predict(model_rf_over, df_testing_over)
# matrice de confusion
matconfus_rf_over <- table(prediction_rf_over, df_testing_over$stroke)
matconfus_rf_over
prediction_rf_over avcN avcP
avcN 847 70
avcP 126 903
# calcul de l'erreur de prediction
err_rf_over <- 1-(sum(diag(matconfus_rf_over)) / sum(matconfus_rf_over))
err_rf_over*100
[1] 10.07194
# kappa / sensibilité / spécificité
kappa_rf_over <- CohenKappa(matconfus_rf_over)
sensitivity_rf_over <- sensitivity(prediction_rf_over, df_testing_over$stroke)
specificity_rf_over <- specificity(prediction_rf_over, df_testing_over$stroke)
print("SVM")
[1] "SVM"
# apprentissage
model_svm_over_quali=svm(stroke~., data=df_training_over_quali, cost=0.5, kernel="linear")
# prediction
prediction_svm_over_quali <- predict(model_svm_over_quali, df_testing_over_quali)
# matrice de confusion
matconfus_svm_over_quali <- table(prediction_svm_over_quali, df_testing_over_quali$stroke)
matconfus_svm_over_quali
prediction_svm_over_quali avcN avcP
avcN 747 132
avcP 226 841
# calcul de l'erreur de prediction
err_svm_over_quali <- 1-(sum(diag(matconfus_svm_over_quali)) / sum(matconfus_svm_over_quali))
err_svm_over_quali*100
[1] 18.39671
# kappa / sensibilité / spécificité
kappa_svm_over_quali <- CohenKappa(matconfus_svm_over_quali)
sensitivity_svm_over_quali <- sensitivity(prediction_svm_over_quali, df_testing_over_quali$stroke)
specificity_svm_over_quali <- specificity(prediction_svm_over_quali, df_testing_over_quali$stroke)
print("Random forest")
[1] "Random forest"
# apprentissage
model_rf_over_quali <- train(stroke ~.,
data = df_training_over_quali,
method = "rf",
metric = "ROC",
preProcess = c("scale", "center"),
trControl = df_control)
# prediction
prediction_rf_over_quali <- predict(model_rf_over_quali, df_testing_over_quali)
# matrice de confusion
matconfus_rf_over_quali <- table(prediction_rf_over_quali, df_testing_over_quali$stroke)
matconfus_rf_over_quali
prediction_rf_over_quali avcN avcP
avcN 797 120
avcP 176 853
# calcul de l'erreur de prediction
err_rf_over_quali <- 1-(sum(diag(matconfus_rf_over_quali)) / sum(matconfus_rf_over_quali))
err_rf_over_quali*100
[1] 15.21069
# kappa / sensibilité / spécificité
kappa_rf_over_quali <- CohenKappa(matconfus_rf_over_quali)
sensitivity_rf_over_quali <- sensitivity(prediction_rf_over_quali, df_testing_over_quali$stroke)
specificity_rf_over_quali <- specificity(prediction_rf_over_quali, df_testing_over_quali$stroke)
print("SVM")
[1] "SVM"
# apprentissage
model_svm_under=svm(stroke~., data=df_training_under, cost=0.5, kernel="linear")
# prediction
prediction_svm_under <- predict(model_svm_under, df_testing_under)
# matrice de confusion
matconfus_svm_under <- table(prediction_svm_under, df_testing_under$stroke)
matconfus_svm_under
prediction_svm_under avcN avcP
avcN 38 8
avcP 16 46
# calcul de l'erreur de prediction
err_svm_under <- 1-(sum(diag(matconfus_svm_under)) / sum(matconfus_svm_under))
err_svm_under*100
[1] 22.22222
# kappa / sensibilité / spécificité
kappa_svm_under <- CohenKappa(matconfus_svm_under)
sensitivity_svm_under <- sensitivity(prediction_svm_under, df_testing_under$stroke)
specificity_svm_under <- specificity(prediction_svm_under, df_testing_under$stroke)
print("Random forest")
[1] "Random forest"
# apprentissage
model_rf_under <- train(stroke ~.,
data = df_training_under,
method = "rf",
metric = "ROC",
preProcess = c("scale", "center"),
trControl = df_control)
# prediction
prediction_rf_under <- predict(model_rf_under, df_testing_under)
# matrice de confusion
matconfus_rf_under <- table(prediction_rf_under, df_testing_under$stroke)
matconfus_rf_under
prediction_rf_under avcN avcP
avcN 36 7
avcP 18 47
# calcul de l'erreur de prediction
err_rf_under <- 1-(sum(diag(matconfus_rf_under)) / sum(matconfus_rf_under))
err_rf_under*100
[1] 23.14815
# kappa / sensibilité / spécificité
kappa_rf_under <- CohenKappa(matconfus_rf_under)
sensitivity_rf_under <- sensitivity(prediction_rf_under, df_testing_under$stroke)
specificity_rf_under <- specificity(prediction_rf_under, df_testing_under$stroke)
Methode <- c("svm unbalanced","rf unbalanced","svm oversample","rf oversample","svm oversample quali","rf oversample quali","svm undersample","rf undersample")
Erreur <- c(round(err_svm_unbalanced*100,2),
round(err_rf_unbalanced*100,2),
round(err_svm_over*100,2),
round(err_rf_over*100,2),
round(err_svm_over_quali*100,2),
round(err_rf_over_quali*100,2),
round(err_svm_under*100,2),
round(err_rf_under*100,2)
)
Kappa <- c(round(kappa_svm_unbalanced,2),
round(kappa_rf_unbalanced,2),
round(kappa_svm_over,2),
round(kappa_rf_over,2),
round(kappa_svm_over_quali,2),
round(kappa_rf_over_quali,2),
round(kappa_svm_under,2),
round(kappa_rf_under,2)
)
Sensibilité <- c(round(sensitivity_svm_unbalanced,2),
round(sensitivity_rf_unbalanced,2),
round(sensitivity_svm_over,2),
round(sensitivity_rf_over,2),
round(sensitivity_svm_over_quali,2),
round(sensitivity_rf_over_quali,2),
round(sensitivity_svm_under,2),
round(sensitivity_rf_under,2)
)
Specificité <- c(round(specificity_svm_unbalanced,2),
round(specificity_rf_unbalanced,2),
round(specificity_svm_over,2),
round(specificity_rf_over,2),
round(specificity_svm_over_quali,2),
round(specificity_rf_over_quali,2),
round(specificity_svm_under,2),
round(specificity_rf_under,2)
)
dataset_compare <- data.frame(Methode, Erreur, Kappa, Sensibilité, Specificité)
dataset_compare
# apprentissage
model_glm <- glm(stroke ~., data=df_training_over, family=binomial(link="logit"))
summary(model_glm)
Call:
glm(formula = stroke ~ ., family = binomial(link = "logit"),
data = df_training_over)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1773 -0.4828 0.0041 0.5427 3.1212
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -6.8709559 0.3648867 -18.830 < 0.0000000000000002 ***
genderMale -1.0662707 0.0983015 -10.847 < 0.0000000000000002 ***
age 0.1151189 0.0037834 30.427 < 0.0000000000000002 ***
hypertensionhtP -0.7641718 0.1287053 -5.937 0.000000002896 ***
heart_diseasehdP -0.7377832 0.1638703 -4.502 0.000006724129 ***
ever_marriedYes -1.0965048 0.1316301 -8.330 < 0.0000000000000002 ***
work_typegovt_job 3.0327952 0.3393028 8.938 < 0.0000000000000002 ***
work_typeprivate 0.5822235 0.1282964 4.538 0.000005676031 ***
work_typeself_employed -0.9772790 0.1573056 -6.213 0.000000000521 ***
Residence_typeUrban -0.7840599 0.0891975 -8.790 < 0.0000000000000002 ***
avg_glucose_level 0.0099685 0.0008479 11.757 < 0.0000000000000002 ***
bmi 0.0079304 0.0075249 1.054 0.291936
smoking_statusnever -0.3544812 0.0957679 -3.701 0.000214 ***
smoking_statussmokes -0.8333663 0.1456016 -5.724 0.000000010429 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 6299.3 on 4543 degrees of freedom
Residual deviance: 3365.3 on 4530 degrees of freedom
AIC: 3393.3
Number of Fisher Scoring iterations: 6
# prediction
prediction_glm <- predict(model_glm, df_testing_over, type="response")
prediction_glm <- ifelse(prediction_glm<0.5,"avcN","avcP")
# matrice de confusion
matconfus_glm <- table(prediction_glm, df_testing_over$stroke)
matconfus_glm
prediction_glm avcN avcP
avcN 803 154
avcP 170 819
# calcul de l'erreur de prediction
err_glm <- 1-(sum(diag(matconfus_glm)) / sum(matconfus_glm))
err_glm*100
[1] 16.64954
# kappa / sensibilité / specificté
kappa_glm <- CohenKappa(matconfus_glm)
kappa_glm
[1] 0.6670092
sensitivity_glm <- sensitivity(factor(prediction_glm), df_testing_over$stroke)
sensitivity_glm
[1] 0.8252826
specificity_glm <- specificity(factor(prediction_glm), df_testing_over$stroke)
specificity_glm
[1] 0.8417266
# apprentissage et tuning
set.seed(42)
model_glm_tuned = train(stroke ~.,
df_training_over,
method = "glmStepAIC", ## selection forward par minimisation de l'AIC
metric="ROC",
preProcess = c("scale", "center"),
trControl = df_control)
Start: AIC=2718.19
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 2691.5 2717.5
<none> 2690.2 2718.2
- smoking_statusnever 1 2698.9 2724.9
- heart_diseasehdP 1 2702.4 2728.4
- work_typeprivate 1 2712.6 2738.6
- hypertensionhtP 1 2712.7 2738.7
- work_typeself_employed 1 2718.1 2744.1
- smoking_statussmokes 1 2721.0 2747.0
- ever_marriedYes 1 2755.3 2781.3
- Residence_typeUrban 1 2758.8 2784.8
- work_typegovt_job 1 2775.2 2801.2
- genderMale 1 2782.2 2808.2
- avg_glucose_level 1 2809.0 2835.0
- age 1 4014.0 4040.0
Step: AIC=2717.45
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 2691.5 2717.5
- smoking_statusnever 1 2700.2 2724.2
- heart_diseasehdP 1 2704.0 2728.0
- hypertensionhtP 1 2713.5 2737.5
- work_typeprivate 1 2714.2 2738.2
- work_typeself_employed 1 2719.3 2743.3
- smoking_statussmokes 1 2722.3 2746.3
- ever_marriedYes 1 2755.9 2779.9
- Residence_typeUrban 1 2759.9 2783.9
- work_typegovt_job 1 2775.7 2799.7
- genderMale 1 2783.8 2807.8
- avg_glucose_level 1 2827.1 2851.1
- age 1 4029.8 4053.8
Start: AIC=2727.64
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 2700.9 2726.9
<none> 2699.6 2727.6
- smoking_statusnever 1 2709.8 2735.8
- work_typeprivate 1 2718.9 2744.9
- heart_diseasehdP 1 2720.0 2746.0
- smoking_statussmokes 1 2720.7 2746.7
- work_typeself_employed 1 2724.1 2750.1
- hypertensionhtP 1 2729.3 2755.3
- ever_marriedYes 1 2766.6 2792.6
- Residence_typeUrban 1 2770.4 2796.4
- work_typegovt_job 1 2785.6 2811.6
- genderMale 1 2796.4 2822.4
- avg_glucose_level 1 2823.6 2849.6
- age 1 4053.5 4079.5
Step: AIC=2726.92
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 2700.9 2726.9
- smoking_statusnever 1 2711.1 2735.1
- work_typeprivate 1 2720.5 2744.5
- heart_diseasehdP 1 2721.8 2745.8
- smoking_statussmokes 1 2721.9 2745.9
- work_typeself_employed 1 2725.3 2749.3
- hypertensionhtP 1 2730.0 2754.0
- ever_marriedYes 1 2767.0 2791.0
- Residence_typeUrban 1 2771.3 2795.3
- work_typegovt_job 1 2786.0 2810.0
- genderMale 1 2798.8 2822.8
- avg_glucose_level 1 2843.3 2867.3
- age 1 4066.7 4090.7
Start: AIC=2665.18
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 2638.1 2664.1
<none> 2637.2 2665.2
- work_typeprivate 1 2647.5 2673.5
- smoking_statusnever 1 2650.3 2676.3
- heart_diseasehdP 1 2657.0 2683.0
- smoking_statussmokes 1 2659.8 2685.8
- work_typeself_employed 1 2674.6 2700.6
- hypertensionhtP 1 2676.6 2702.6
- ever_marriedYes 1 2691.5 2717.5
- Residence_typeUrban 1 2700.3 2726.3
- work_typegovt_job 1 2709.0 2735.0
- genderMale 1 2733.9 2759.9
- avg_glucose_level 1 2766.5 2792.5
- age 1 4018.8 4044.8
Step: AIC=2664.12
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 2638.1 2664.1
- work_typeprivate 1 2648.6 2672.6
- smoking_statusnever 1 2651.3 2675.3
- heart_diseasehdP 1 2658.3 2682.3
- smoking_statussmokes 1 2660.8 2684.8
- work_typeself_employed 1 2675.3 2699.3
- hypertensionhtP 1 2677.0 2701.0
- ever_marriedYes 1 2692.1 2716.1
- Residence_typeUrban 1 2701.0 2725.0
- work_typegovt_job 1 2709.3 2733.3
- genderMale 1 2735.2 2759.2
- avg_glucose_level 1 2784.8 2808.8
- age 1 4034.4 4058.4
Start: AIC=2712.47
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 2685.0 2711.0
<none> 2684.5 2712.5
- smoking_statusnever 1 2694.9 2720.9
- work_typeprivate 1 2703.0 2729.0
- heart_diseasehdP 1 2703.2 2729.2
- hypertensionhtP 1 2708.8 2734.8
- smoking_statussmokes 1 2714.7 2740.7
- work_typeself_employed 1 2719.5 2745.5
- ever_marriedYes 1 2735.8 2761.8
- Residence_typeUrban 1 2738.7 2764.7
- work_typegovt_job 1 2772.7 2798.7
- genderMale 1 2788.4 2814.4
- avg_glucose_level 1 2792.4 2818.4
- age 1 4029.9 4055.9
Step: AIC=2711
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 2685.0 2711.0
- smoking_statusnever 1 2695.5 2719.5
- work_typeprivate 1 2703.6 2727.6
- heart_diseasehdP 1 2703.9 2727.9
- hypertensionhtP 1 2709.1 2733.1
- smoking_statussmokes 1 2715.3 2739.3
- work_typeself_employed 1 2720.1 2744.1
- ever_marriedYes 1 2736.0 2760.0
- Residence_typeUrban 1 2739.3 2763.3
- work_typegovt_job 1 2772.7 2796.7
- genderMale 1 2789.6 2813.6
- avg_glucose_level 1 2809.0 2833.0
- age 1 4048.3 4072.3
Start: AIC=2766.67
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 2739.3 2765.3
<none> 2738.7 2766.7
- heart_diseasehdP 1 2748.4 2774.4
- smoking_statusnever 1 2751.6 2777.6
- work_typeprivate 1 2752.5 2778.5
- hypertensionhtP 1 2765.0 2791.0
- smoking_statussmokes 1 2769.9 2795.9
- work_typeself_employed 1 2770.9 2796.9
- Residence_typeUrban 1 2796.0 2822.0
- ever_marriedYes 1 2798.8 2824.8
- work_typegovt_job 1 2824.9 2850.9
- genderMale 1 2839.9 2865.9
- avg_glucose_level 1 2851.8 2877.8
- age 1 4036.8 4062.8
Step: AIC=2765.29
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 2739.3 2765.3
- heart_diseasehdP 1 2749.2 2773.2
- smoking_statusnever 1 2752.2 2776.2
- work_typeprivate 1 2753.3 2777.3
- hypertensionhtP 1 2765.2 2789.2
- smoking_statussmokes 1 2770.7 2794.7
- work_typeself_employed 1 2771.4 2795.4
- Residence_typeUrban 1 2796.4 2820.4
- ever_marriedYes 1 2798.9 2822.9
- work_typegovt_job 1 2825.1 2849.1
- genderMale 1 2840.8 2864.8
- avg_glucose_level 1 2865.9 2889.9
- age 1 4050.8 4074.8
Start: AIC=3393.33
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
bmi + smoking_statusnever + smoking_statussmokes
Df Deviance AIC
- bmi 1 3366.4 3392.4
<none> 3365.3 3393.3
- smoking_statusnever 1 3379.1 3405.1
- heart_diseasehdP 1 3385.2 3411.2
- work_typeprivate 1 3386.0 3412.0
- smoking_statussmokes 1 3399.1 3425.1
- hypertensionhtP 1 3400.5 3426.5
- work_typeself_employed 1 3404.4 3430.4
- ever_marriedYes 1 3439.5 3465.5
- Residence_typeUrban 1 3443.8 3469.8
- work_typegovt_job 1 3469.7 3495.7
- genderMale 1 3488.1 3514.1
- avg_glucose_level 1 3513.4 3539.4
- age 1 5040.9 5066.9
Step: AIC=3392.44
.outcome ~ genderMale + age + hypertensionhtP + heart_diseasehdP +
ever_marriedYes + work_typegovt_job + work_typeprivate +
work_typeself_employed + Residence_typeUrban + avg_glucose_level +
smoking_statusnever + smoking_statussmokes
Df Deviance AIC
<none> 3366.4 3392.4
- smoking_statusnever 1 3380.2 3404.2
- heart_diseasehdP 1 3386.6 3410.6
- work_typeprivate 1 3387.4 3411.4
- smoking_statussmokes 1 3400.2 3424.2
- hypertensionhtP 1 3401.0 3425.0
- work_typeself_employed 1 3405.4 3429.4
- ever_marriedYes 1 3439.9 3463.9
- Residence_typeUrban 1 3444.7 3468.7
- work_typegovt_job 1 3470.1 3494.1
- genderMale 1 3489.8 3513.8
- avg_glucose_level 1 3534.9 3558.9
- age 1 5060.2 5084.2
summary(model_glm_tuned$finalModel)
Call:
NULL
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1941 -0.4831 0.0043 0.5404 3.1156
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.10620 0.04610 -2.303 0.021254 *
genderMale -0.48971 0.04504 -10.874 < 0.0000000000000002 ***
age 2.12686 0.06906 30.799 < 0.0000000000000002 ***
hypertensionhtP -0.24161 0.04106 -5.885 0.000000003985 ***
heart_diseasehdP -0.19141 0.04213 -4.543 0.000005544759 ***
ever_marriedYes -0.45106 0.05432 -8.303 < 0.0000000000000002 ***
work_typegovt_job 0.70053 0.07796 8.986 < 0.0000000000000002 ***
work_typeprivate 0.27665 0.06054 4.569 0.000004893163 ***
work_typeself_employed -0.34208 0.05515 -6.203 0.000000000553 ***
Residence_typeUrban -0.38522 0.04389 -8.777 < 0.0000000000000002 ***
avg_glucose_level 0.58826 0.04695 12.530 < 0.0000000000000002 ***
smoking_statusnever -0.17734 0.04781 -3.709 0.000208 ***
smoking_statussmokes -0.28716 0.05012 -5.729 0.000000010094 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 6299.3 on 4543 degrees of freedom
Residual deviance: 3366.4 on 4531 degrees of freedom
AIC: 3392.4
Number of Fisher Scoring iterations: 6
# prediction
prediction_glm_tuned <- predict(model_glm_tuned, df_testing_over)
# matrice de confusion
matconfus_glm_tuned <- table(prediction_glm_tuned, df_testing_over$stroke)
matconfus_glm_tuned
prediction_glm_tuned avcN avcP
avcN 803 151
avcP 170 822
# calcul de l'erreur de prediction
err_glm_tuned <- 1-(sum(diag(matconfus_glm_tuned)) / sum(matconfus_glm_tuned))
err_glm_tuned*100
[1] 16.49538
# kappa / sensibilité / specificté
kappa_glm_tuned <- CohenKappa(matconfus_glm_tuned)
kappa_glm_tuned
[1] 0.6700925
sensitivity_glm_tuned <- sensitivity(prediction_glm_tuned, df_testing_over$stroke)
sensitivity_glm_tuned
[1] 0.8252826
specificity_glm_tuned <- specificity(prediction_glm_tuned, df_testing_over$stroke)
specificity_glm_tuned
[1] 0.8448099
# apprentissage et tuning
set.seed(42)
grid <- expand.grid(.k=seq(1,50,by=1))
model_knn_tuned <- train(stroke ~.,
data = df_training_over,
method = "knn",
metric = "ROC",
preProcess = c('center', 'scale'),
tuneGrid= grid,
trControl = df_control)
# affichage du modele
model_knn_tuned$finalModel
12-nearest neighbor model
Training set outcome distribution:
avcN avcP
2272 2272
k = model_knn_tuned$results$k[model_knn_tuned$results$ROC == max(model_knn_tuned$results$ROC)]
ggplot(model_knn_tuned)+geom_vline(xintercept = k,color ='red') # performance metric (ROC) en fonction des valeurs de C (le parametre a optimiser)
# prediction
prediction_knn_tuned <- predict(model_knn_tuned, df_testing_over,type="prob")
prediction_knn_tuned <- predict(model_knn_tuned, df_testing_over)#,type="prob")
# matrice de confusion
matconfus_knn_tuned <- table(prediction_knn_tuned, df_testing_over$stroke)
matconfus_knn_tuned
prediction_knn_tuned avcN avcP
avcN 778 127
avcP 195 846
# calcul de l'erreur de prediction
err_knn_tuned <- 1-(sum(diag(matconfus_knn_tuned)) / sum(matconfus_knn_tuned))
err_knn_tuned*100
[1] 16.54676
# kappa / sensibilité / specificté
kappa_knn_tuned <- CohenKappa(matconfus_knn_tuned)
kappa_knn_tuned
[1] 0.6690647
sensitivity_knn_tuned <- sensitivity(prediction_knn_tuned, df_testing_over$stroke)
sensitivity_knn_tuned
[1] 0.7995889
specificity_knn_tuned <- specificity(prediction_knn_tuned, df_testing_over$stroke)
specificity_knn_tuned
[1] 0.8694758
Nombre de cluster opt
k
[1] 12
# apprentissage
model_svm=svm(stroke~., data=df_training_over, cost=0.5, kernel="linear")
# prediction
prediction_svm <- predict(model_svm, df_testing_over)
# matrice de confusion
matconfus_svm <- table(prediction_svm, df_testing_over$stroke)
matconfus_svm
prediction_svm avcN avcP
avcN 799 148
avcP 174 825
# calcul de l'erreur de prediction
err_svm <- 1-(sum(diag(matconfus_svm)) / sum(matconfus_svm))
err_svm*100
[1] 16.54676
# kappa / sensibilité / specificté
kappa_svm <- CohenKappa(matconfus_svm)
kappa_svm
[1] 0.6690647
sensitivity_svm <- sensitivity(prediction_svm, df_testing_over$stroke)
sensitivity_svm
[1] 0.8211716
specificity_svm <- specificity(prediction_svm, df_testing_over$stroke)
specificity_svm
[1] 0.8478931
# apprentissage et tuning
grid <- expand.grid(C=seq(0.01,1,0.03))
model_svm_tuned <- train(stroke ~., data = df_training_over,
method = "svmLinear",
metric = "ROC",
preProcess = c("scale", "center"),
tuneGrid= grid,
trControl = df_control)
# affichage du modele
model_svm_tuned$finalModel
Support Vector Machine object of class "ksvm"
SV type: C-svc (classification)
parameter : cost C = 0.13
Linear (vanilla) kernel function.
Number of Support Vectors : 1824
Objective Function Value : -234.5989
Training error : 0.164173
Probability model included.
c = model_svm_tuned$results$C[model_svm_tuned$results$ROC == max(model_svm_tuned$results$ROC)]
ggplot(model_svm_tuned)+geom_vline(xintercept = c,color ='red')
# prediction
prediction_svm_tuned <- predict(model_svm_tuned, df_testing_over)
# matrice de confusion
matconfus_svm_tuned <- table(prediction_svm_tuned, df_testing_over$stroke)
matconfus_svm_tuned
prediction_svm_tuned avcN avcP
avcN 801 149
avcP 172 824
# calcul de l'erreur de prediction
err_svm_tuned <- 1-(sum(diag(matconfus_svm_tuned)) / sum(matconfus_svm_tuned))
err_svm_tuned*100
[1] 16.49538
# kappa / sensibilité / specificté
kappa_svm_tuned <- CohenKappa(matconfus_svm_tuned)
kappa_svm_tuned
[1] 0.6700925
sensitivity_svm_tuned <- sensitivity(prediction_svm_tuned, df_testing_over$stroke)
sensitivity_svm_tuned
[1] 0.8232271
specificity_svm_tuned <- specificity(prediction_svm_tuned, df_testing_over$stroke)
specificity_svm_tuned
[1] 0.8468654
Coût
c
[1] 0.22
# apprentissage
tree <- rpart(stroke ~.,data=df_training_over, method="class",
control=rpart.control(minsplit=1,cp=0,xval=10))
# affichage du modele
plot(tree)
text(tree, use.n.=TRUE, all=TRUE, cex=.8)
Warning in text.default(xy$x, xy$y + 0.5 * cxy[2L], rows[left.child], ...) :
"use.n." n'est pas un paramètre graphique
Warning in text.default(xy$x[leaves], xy$y[leaves] - 0.5 * cxy[2L], stat, :
"use.n." n'est pas un paramètre graphique
# prediction
prediction_tree <- predict(tree, df_testing_over, type="class")
# matrice de confusion
matconfus_tree <- table(prediction_tree, df_testing_over$stroke)
matconfus_tree
prediction_tree avcN avcP
avcN 816 140
avcP 157 833
# calcul de l'erreur de prediction
err_tree <- 1-(sum(diag(matconfus_tree)) / sum(matconfus_tree))
err_tree*100
[1] 15.26208
# kappa / sensibilité / specificté
kappa_tree <- CohenKappa(matconfus_tree)
kappa_tree
[1] 0.6947585
sensitivity_tree <- sensitivity(prediction_tree, df_testing_over$stroke)
sensitivity_tree
[1] 0.8386434
specificity_tree <- specificity(prediction_tree, df_testing_over$stroke)
specificity_tree
[1] 0.8561151
# apprentissage et tuning
grid <- expand.grid(cp=seq(0.01,1,0.01))
model_tree_tuned = train(stroke ~.,
data=df_training_over,
method="rpart",
metric = "ROC",
preProcess = c("scale", "center"),
tuneGrid= grid,
trControl = df_control)
# affichage du modele
model_tree_tuned$finalModel
n= 4544
node), split, n, loss, yval, (yprob)
* denotes terminal node
1) root 4544 2272 avcN (0.5000000 0.5000000)
2) age< -0.2110262 1612 163 avcN (0.8988834 0.1011166) *
3) age>=-0.2110262 2932 823 avcP (0.2806958 0.7193042)
6) work_typeself_employed>=1.019409 474 210 avcN (0.5569620 0.4430380) *
7) work_typeself_employed< 1.019409 2458 559 avcP (0.2274207 0.7725793)
14) age< 0.2728072 609 267 avcP (0.4384236 0.5615764)
28) avg_glucose_level< -0.6871567 149 37 avcN (0.7516779 0.2483221) *
29) avg_glucose_level>=-0.6871567 460 155 avcP (0.3369565 0.6630435) *
15) age>=0.2728072 1849 292 avcP (0.1579232 0.8420768) *
summary(model_tree_tuned$finalModel)
Call:
(function (formula, data, weights, subset, na.action = na.rpart,
method, model = FALSE, x = FALSE, y = TRUE, parms, control,
cost, ...)
{
Call <- match.call()
if (is.data.frame(model)) {
m <- model
model <- FALSE
}
else {
indx <- match(c("formula", "data", "weights", "subset"),
names(Call), nomatch = 0)
if (indx[1] == 0)
stop("a 'formula' argument is required")
temp <- Call[c(1, indx)]
temp$na.action <- na.action
temp[[1]] <- quote(stats::model.frame)
m <- eval.parent(temp)
}
Terms <- attr(m, "terms")
if (any(attr(Terms, "order") > 1))
stop("Trees cannot handle interaction terms")
Y <- model.response(m)
wt <- model.weights(m)
if (any(wt < 0))
stop("negative weights not allowed")
if (!length(wt))
wt <- rep(1, nrow(m))
offset <- model.offset(m)
X <- rpart.matrix(m)
nobs <- nrow(X)
nvar <- ncol(X)
if (missing(method)) {
method <- if (is.factor(Y) || is.character(Y))
"class"
else if (inherits(Y, "Surv"))
"exp"
else if (is.matrix(Y))
"poisson"
else "anova"
}
if (is.list(method)) {
mlist <- method
method <- "user"
init <- if (missing(parms))
mlist$init(Y, offset, wt = wt)
else mlist$init(Y, offset, parms, wt)
keep <- rpartcallback(mlist, nobs, init)
method.int <- 4
parms <- init$parms
}
else {
method.int <- pmatch(method, c("anova", "poisson", "class",
"exp"))
if (is.na(method.int))
stop("Invalid method")
method <- c("anova", "poisson", "class", "exp")[method.int]
if (method.int == 4)
method.int <- 2
init <- if (missing(parms))
get(paste("rpart", method, sep = "."), envir = environment())(Y,
offset, , wt)
else get(paste("rpart", method, sep = "."), envir = environment())(Y,
offset, parms, wt)
ns <- asNamespace("rpart")
if (!is.null(init$print))
environment(init$print) <- ns
if (!is.null(init$summary))
environment(init$summary) <- ns
if (!is.null(init$text))
environment(init$text) <- ns
}
Y <- init$y
xlevels <- .getXlevels(Terms, m)
cats <- rep(0, ncol(X))
if (!is.null(xlevels))
cats[match(names(xlevels), colnames(X))] <- unlist(lapply(xlevels,
length))
extraArgs <- list(...)
if (length(extraArgs)) {
controlargs <- names(formals(rpart.control))
indx <- match(names(extraArgs), controlargs, nomatch = 0)
if (any(indx == 0))
stop(gettextf("Argument %s not matched", names(extraArgs)[indx ==
0]), domain = NA)
}
controls <- rpart.control(...)
if (!missing(control))
controls[names(control)] <- control
xval <- controls$xval
if (is.null(xval) || (length(xval) == 1 && xval == 0) ||
method == "user") {
xgroups <- 0
xval <- 0
}
else if (length(xval) == 1) {
xgroups <- sample(rep(1:xval, length = nobs), nobs, replace = FALSE)
}
else if (length(xval) == nobs) {
xgroups <- xval
xval <- length(unique(xgroups))
}
else {
if (!is.null(attr(m, "na.action"))) {
temp <- as.integer(attr(m, "na.action"))
xval <- xval[-temp]
if (length(xval) == nobs) {
xgroups <- xval
xval <- length(unique(xgroups))
}
else stop("Wrong length for 'xval'")
}
else stop("Wrong length for 'xval'")
}
if (missing(cost))
cost <- rep(1, nvar)
else {
if (length(cost) != nvar)
stop("Cost vector is the wrong length")
if (any(cost <= 0))
stop("Cost vector must be positive")
}
tfun <- function(x) if (is.matrix(x))
rep(is.ordered(x), ncol(x))
else is.ordered(x)
labs <- sub("^`(.*)`$", "\\1", attr(Terms, "term.labels"))
isord <- unlist(lapply(m[labs], tfun))
storage.mode(X) <- "double"
storage.mode(wt) <- "double"
temp <- as.double(unlist(init$parms))
if (!length(temp))
temp <- 0
rpfit <- .Call(C_rpart, ncat = as.integer(cats * !isord),
method = as.integer(method.int), as.double(unlist(controls)),
temp, as.integer(xval), as.integer(xgroups), as.double(t(init$y)),
X, wt, as.integer(init$numy), as.double(cost))
nsplit <- nrow(rpfit$isplit)
ncat <- if (!is.null(rpfit$csplit))
nrow(rpfit$csplit)
else 0
if (nsplit == 0)
xval <- 0
numcp <- ncol(rpfit$cptable)
temp <- if (nrow(rpfit$cptable) == 3)
c("CP", "nsplit", "rel error")
else c("CP", "nsplit", "rel error", "xerror", "xstd")
dimnames(rpfit$cptable) <- list(temp, 1:numcp)
tname <- c("<leaf>", colnames(X))
splits <- matrix(c(rpfit$isplit[, 2:3], rpfit$dsplit), ncol = 5,
dimnames = list(tname[rpfit$isplit[, 1] + 1], c("count",
"ncat", "improve", "index", "adj")))
index <- rpfit$inode[, 2]
nadd <- sum(isord[rpfit$isplit[, 1]])
if (nadd > 0) {
newc <- matrix(0, nadd, max(cats))
cvar <- rpfit$isplit[, 1]
indx <- isord[cvar]
cdir <- splits[indx, 2]
ccut <- floor(splits[indx, 4])
splits[indx, 2] <- cats[cvar[indx]]
splits[indx, 4] <- ncat + 1:nadd
for (i in 1:nadd) {
newc[i, 1:(cats[(cvar[indx])[i]])] <- -as.integer(cdir[i])
newc[i, 1:ccut[i]] <- as.integer(cdir[i])
}
catmat <- if (ncat == 0)
newc
else {
cs <- rpfit$csplit
ncs <- ncol(cs)
ncc <- ncol(newc)
if (ncs < ncc)
cs <- cbind(cs, matrix(0, nrow(cs), ncc - ncs))
rbind(cs, newc)
}
ncat <- ncat + nadd
}
else catmat <- rpfit$csplit
if (nsplit == 0) {
frame <- data.frame(row.names = 1, var = "<leaf>", n = rpfit$inode[,
5], wt = rpfit$dnode[, 3], dev = rpfit$dnode[, 1],
yval = rpfit$dnode[, 4], complexity = rpfit$dnode[,
2], ncompete = 0, nsurrogate = 0)
}
else {
temp <- ifelse(index == 0, 1, index)
svar <- ifelse(index == 0, 0, rpfit$isplit[temp, 1])
frame <- data.frame(row.names = rpfit$inode[, 1], var = tname[svar +
1], n = rpfit$inode[, 5], wt = rpfit$dnode[, 3],
dev = rpfit$dnode[, 1], yval = rpfit$dnode[, 4],
complexity = rpfit$dnode[, 2], ncompete = pmax(0,
rpfit$inode[, 3] - 1), nsurrogate = rpfit$inode[,
4])
}
if (method.int == 3) {
numclass <- init$numresp - 2
nodeprob <- rpfit$dnode[, numclass + 5]/sum(wt)
temp <- pmax(1, init$counts)
temp <- rpfit$dnode[, 4 + (1:numclass)] %*% diag(init$parms$prior/temp)
yprob <- temp/rowSums(temp)
yval2 <- matrix(rpfit$dnode[, 4 + (0:numclass)], ncol = numclass +
1)
frame$yval2 <- cbind(yval2, yprob, nodeprob)
}
else if (init$numresp > 1)
frame$yval2 <- rpfit$dnode[, -(1:3), drop = FALSE]
if (is.null(init$summary))
stop("Initialization routine is missing the 'summary' function")
functions <- if (is.null(init$print))
list(summary = init$summary)
else list(summary = init$summary, print = init$print)
if (!is.null(init$text))
functions <- c(functions, list(text = init$text))
if (method == "user")
functions <- c(functions, mlist)
where <- rpfit$which
names(where) <- row.names(m)
ans <- list(frame = frame, where = where, call = Call, terms = Terms,
cptable = t(rpfit$cptable), method = method, parms = init$parms,
control = controls, functions = functions, numresp = init$numresp)
if (nsplit)
ans$splits = splits
if (ncat > 0)
ans$csplit <- catmat + 2
if (nsplit)
ans$variable.importance <- importance(ans)
if (model) {
ans$model <- m
if (missing(y))
y <- FALSE
}
if (y)
ans$y <- Y
if (x) {
ans$x <- X
ans$wt <- wt
}
ans$ordered <- isord
if (!is.null(attr(m, "na.action")))
ans$na.action <- attr(m, "na.action")
if (!is.null(xlevels))
attr(ans, "xlevels") <- xlevels
if (method == "class")
attr(ans, "ylevels") <- init$ylevels
class(ans) <- "rpart"
ans
})(formula = .outcome ~ ., data = list(c(1.52992197411931, 1.52992197411931,
-0.653484260302217, -0.653484260302217, 1.52992197411931, -0.653484260302217,
-0.653484260302217, -0.653484260302217, 1.52992197411931, 1.52992197411931,
-0.653484260302217, -0.653484260302217, -0.653484260302217, -0.653484260302217,
1.52992197411931, 1.52992197411931, -0.653484260302217, 1.52992197411931,
-0.653484260302217, 1.52992197411931, -0.653484260302217, 1.52992197411931,
1.52992197411931, 1.52992197411931, 1.52992197411931, -0.653484260302217,
1.52992197411931, -0.653484260302217, -0.653484260302217, -0.653484260302217,
1.52992197411931, -0.653484260302217, 1.52992197411931, -0.653484260302217,
-0.653484260302217, -0.653484260302217, -0.653484260302217, -0.653484260302217,
1.52992197411931, -0.653484260302217, -0.653484260302217, -0.653484260302217,
-0.653484260302217, 1.52992197411931, -0.653484260302217, -0.653484260302217,
-0.653484260302217, 1.52992197411931, -0.653484260302217, 1.52992197411931,
1.52992197411931, -0.653484260302217, -0.653484260302217, 1.52992197411931,
1.52992197411931, -0.653484260302217, -0.653484260302217, -0.653484260302217,
-0.653484260302217, -0.653484260302217, -0.653484260302217, 1.52992197411931,
1.52992197411931, -0.653484260302217, 1.52992197411931, 1.52992197411931,
1.52992197411931, -0.653484260302217, -0.653484260302217, -0.653484260302217,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
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2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2)), control = list(20, 7, 0, 4, 5, 2, 0, 30, 0))
n= 4544
CP nsplit rel error
1 0.56602113 0 1.0000000
2 0.02376761 1 0.4339789
3 0.01650528 2 0.4102113
4 0.01000000 4 0.3772007
Variable importance
age work_typeself_employed smoking_statussmokes avg_glucose_level ever_marriedYes bmi
73 7 6 5 4 4
Node number 1: 4544 observations, complexity param=0.5660211
predicted class=avcN expected loss=0.5 P(node) =1
class counts: 2272 2272
probabilities: 0.500 0.500
left son=2 (1612 obs) right son=3 (2932 obs)
Primary splits:
age < -0.2110262 to the left, improve=794.98930, (0 missing)
avg_glucose_level < 0.8560023 to the left, improve=213.57730, (0 missing)
smoking_statussmokes < 1.054946 to the right, improve=121.20980, (0 missing)
Residence_typeUrban < 0.1792917 to the right, improve=100.78070, (0 missing)
genderMale < 0.4382189 to the right, improve= 83.94916, (0 missing)
Surrogate splits:
smoking_statussmokes < 1.054946 to the right, agree=0.675, adj=0.083, (0 split)
ever_marriedYes < -0.6740588 to the left, agree=0.669, adj=0.067, (0 split)
bmi < -1.401959 to the left, agree=0.667, adj=0.061, (0 split)
avg_glucose_level < -1.032488 to the left, agree=0.655, adj=0.027, (0 split)
Node number 2: 1612 observations
predicted class=avcN expected loss=0.1011166 P(node) =0.3547535
class counts: 1449 163
probabilities: 0.899 0.101
Node number 3: 2932 observations, complexity param=0.02376761
predicted class=avcP expected loss=0.2806958 P(node) =0.6452465
class counts: 823 2109
probabilities: 0.281 0.719
left son=6 (474 obs) right son=7 (2458 obs)
Primary splits:
work_typeself_employed < 1.019409 to the right, improve=86.30702, (0 missing)
age < 0.3803585 to the left, improve=73.82598, (0 missing)
genderMale < 0.4382189 to the right, improve=51.05197, (0 missing)
smoking_statussmokes < 1.054946 to the right, improve=50.41235, (0 missing)
Residence_typeUrban < 0.1792917 to the right, improve=46.97257, (0 missing)
Node number 6: 474 observations
predicted class=avcN expected loss=0.443038 P(node) =0.1043134
class counts: 264 210
probabilities: 0.557 0.443
Node number 7: 2458 observations, complexity param=0.01650528
predicted class=avcP expected loss=0.2274207 P(node) =0.5409331
class counts: 559 1899
probabilities: 0.227 0.773
left son=14 (609 obs) right son=15 (1849 obs)
Primary splits:
age < 0.2728072 to the left, improve=72.08907, (0 missing)
genderMale < 0.4382189 to the right, improve=38.82275, (0 missing)
smoking_statussmokes < 1.054946 to the right, improve=38.23586, (0 missing)
avg_glucose_level < 0.07933248 to the left, improve=34.44747, (0 missing)
Residence_typeUrban < 0.1792917 to the right, improve=31.73493, (0 missing)
Surrogate splits:
bmi < 1.17693 to the right, agree=0.758, adj=0.025, (0 split)
avg_glucose_level < -1.179359 to the left, agree=0.755, adj=0.010, (0 split)
Node number 14: 609 observations, complexity param=0.01650528
predicted class=avcP expected loss=0.4384236 P(node) =0.1340229
class counts: 267 342
probabilities: 0.438 0.562
left son=28 (149 obs) right son=29 (460 obs)
Primary splits:
avg_glucose_level < -0.6871567 to the left, improve=38.71413, (0 missing)
bmi < -0.8783906 to the left, improve=22.40866, (0 missing)
work_typegovt_job < 1.888827 to the left, improve=13.41302, (0 missing)
age < -0.1587752 to the right, improve=10.44095, (0 missing)
genderMale < 0.4382189 to the right, improve=10.08388, (0 missing)
Node number 15: 1849 observations
predicted class=avcP expected loss=0.1579232 P(node) =0.4069102
class counts: 292 1557
probabilities: 0.158 0.842
Node number 28: 149 observations
predicted class=avcN expected loss=0.2483221 P(node) =0.03279049
class counts: 112 37
probabilities: 0.752 0.248
Node number 29: 460 observations
predicted class=avcP expected loss=0.3369565 P(node) =0.1012324
class counts: 155 305
probabilities: 0.337 0.663
plot(model_tree_tuned$finalModel, uniform=TRUE, main="Pruned Classification Tree")
text(model_tree_tuned$finalModel, use.n.=TRUE, all=TRUE, cex=.8)
Warning in text.default(xy$x, xy$y + 0.5 * cxy[2L], rows[left.child], ...) :
"use.n." n'est pas un paramètre graphique
Warning in text.default(xy$x[leaves], xy$y[leaves] - 0.5 * cxy[2L], stat, :
"use.n." n'est pas un paramètre graphique
# prediction
prediction_tree_tuned <- predict(model_tree_tuned, df_testing_over)
# matrice de confusion
matconfus_tree_tuned <- table(prediction_tree_tuned, df_testing_over$stroke)
matconfus_tree_tuned
prediction_tree_tuned avcN avcP
avcN 771 188
avcP 202 785
# calcul de l'erreur de prediction
err_tree_tuned <- 1-(sum(diag(matconfus_tree_tuned)) / sum(matconfus_tree_tuned))
err_tree_tuned*100
[1] 20.04111
# kappa / sensibilité / specificté
kappa_tree_tuned <- CohenKappa(matconfus_tree_tuned)
kappa_tree_tuned
[1] 0.5991778
sensitivity_tree_tuned <- sensitivity(prediction_tree_tuned, df_testing_over$stroke)
sensitivity_tree_tuned
[1] 0.7923947
specificity_tree_tuned <- specificity(prediction_tree_tuned, df_testing_over$stroke)
specificity_tree_tuned
[1] 0.8067831
# apprentissage
model_rf <- train(stroke ~.,
data = df_training_over,
method = "rf",
metric = "ROC",
preProcess = c("scale", "center"),
trControl = df_control)
model_rf$finalModel
Call:
randomForest(x = x, y = y, mtry = min(param$mtry, ncol(x)))
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 7
OOB estimate of error rate: 9.26%
Confusion matrix:
avcN avcP class.error
avcN 2014 258 0.11355634
avcP 163 2109 0.07174296
# prediction
prediction_rf <- predict(model_rf, df_testing_over)
# matrice de confusion
matconfus_rf <- table(prediction_rf, df_testing_over$stroke)
matconfus_rf
prediction_rf avcN avcP
avcN 846 70
avcP 127 903
# calcul de l'erreur de prediction
err_rf <- 1-(sum(diag(matconfus_rf)) / sum(matconfus_rf))
err_rf*100
[1] 10.12333
# kappa / sensibilité / specificté
kappa_rf <- CohenKappa(matconfus_rf)
kappa_rf
[1] 0.7975334
sensitivity_rf <- sensitivity(prediction_rf, df_testing_over$stroke)
sensitivity_rf
[1] 0.8694758
specificity_rf <- specificity(prediction_rf, df_testing_over$stroke)
specificity_rf
[1] 0.9280576
# apprentissage et tuning
grid <- expand.grid(mtry=2:10)
model_rf_tuned <- train(stroke ~.,
data = df_training_over,
method = "rf",
metric = "ROC",
tuneGrid= grid,
preProcess = c("scale", "center"),
trControl = df_control)
# affichage du modele
plot(model_rf_tuned)
plot(model_rf_tuned$finalModel)
model_rf_tuned$finalModel
Call:
randomForest(x = x, y = y, mtry = min(param$mtry, ncol(x)))
Type of random forest: classification
Number of trees: 500
No. of variables tried at each split: 6
OOB estimate of error rate: 9.42%
Confusion matrix:
avcN avcP class.error
avcN 2010 262 0.11531690
avcP 166 2106 0.07306338
varImpPlot(model_rf_tuned$finalModel, sort = TRUE, n.var = 13, main = "Variables importance")
# prediction
prediction_rf_tuned <- predict(model_rf_tuned, df_testing_over)
# matrice de confusion
matconfus_rf_tuned <- table(prediction_rf_tuned, df_testing_over$stroke)
matconfus_rf_tuned
prediction_rf_tuned avcN avcP
avcN 849 70
avcP 124 903
# calcul de l'erreur de prediction
err_rf_tuned <- 1-(sum(diag(matconfus_rf_tuned)) / sum(matconfus_rf_tuned))
err_rf_tuned*100
[1] 9.969168
# kappa / sensibilité / specificté
kappa_rf_tuned <- CohenKappa(matconfus_rf_tuned)
kappa_rf_tuned
[1] 0.8006166
sensitivity_rf_tuned <- sensitivity(prediction_rf_tuned, df_testing_over$stroke)
sensitivity_rf_tuned
[1] 0.8725591
specificity_rf_tuned <- specificity(prediction_rf_tuned, df_testing_over$stroke)
specificity_rf_tuned
[1] 0.9280576
### les courbes ROC des 5 méthodes
# glm
pr_glm = prediction(as.numeric(prediction_glm_tuned), as.numeric(df_testing_over$stroke))
prf_glm = performance(pr_glm, measure = "tpr", x.measure = "fpr")
plot(prf_glm)
# knn
pr_knn = prediction(as.numeric(prediction_knn_tuned), as.numeric(df_testing_over$stroke))
prf_knn = performance(pr_knn, measure = "tpr", x.measure = "fpr")
plot(prf_knn, col="red", add=TRUE)
# tree
pr_tree = prediction(as.numeric(prediction_tree_tuned), as.numeric(df_testing_over$stroke))
prf_tree = performance(pr_tree, measure = "tpr", x.measure = "fpr")
plot(prf_tree, col="purple",add=TRUE)
# rf
pr_rf = prediction(as.numeric(prediction_rf_tuned), as.numeric(df_testing_over$stroke))
prf_rf = performance(pr_rf, measure = "tpr", x.measure = "fpr")
plot(prf_rf, col="green", add=TRUE)
# svm
pr_svm = prediction(as.numeric(prediction_svm_tuned), as.numeric(df_testing_over$stroke))
prf_svm = performance(pr_svm, measure = "tpr", x.measure = "fpr")
plot(prf_svm, col="blue", add=TRUE)
legend("bottomright", inset = 0.1, legend = c("glm", "knn", "tree", "rf", "svm"), lty = 1,
col = c("black", "red", "purple","green","blue"))
### les AUC pour les 5 methodes
# glm
auc_glm = performance(pr_glm, measure = "auc")
auc_glm = auc_glm@y.values[[1]]
# knn
auc_knn = performance(pr_knn, measure = "auc")
auc_knn = auc_knn@y.values[[1]]
# tree
auc_tree = performance(pr_tree, measure = "auc")
auc_tree = auc_tree@y.values[[1]]
# rf
auc_rf = performance(pr_rf, measure = "auc")
auc_rf = auc_rf@y.values[[1]]
# svm
auc_svm = performance(pr_svm, measure = "auc")
auc_svm = auc_svm@y.values[[1]]
Methode <- c("glm","glm_tuned","knn_tuned","svm","svm_tuned","tree","tree_tuned","rf","rf_tuned")
Erreur <- c(round(err_glm*100,2),
round(err_glm_tuned*100,2),
round(err_knn_tuned*100,2),
round(err_svm*100,2),
round(err_svm_tuned*100,2),
round(err_tree*100,2),
round(err_tree_tuned*100,2),
round(err_rf*100,2),
round(err_rf_tuned*100,2)
)
Kappa <- c(round(kappa_glm,2),
round(kappa_glm_tuned,2),
round(kappa_knn_tuned,2),
round(kappa_svm,2),
round(kappa_svm_tuned,2),
round(kappa_tree,2),
round(kappa_tree_tuned,2),
round(kappa_rf,2),
round(kappa_rf_tuned,2)
)
Sensibilité <- c(round(sensitivity_glm,2),
round(sensitivity_glm_tuned,2),
round(sensitivity_knn_tuned,2),
round(sensitivity_svm,2),
round(sensitivity_svm_tuned,2),
round(sensitivity_tree,2),
round(sensitivity_tree_tuned,2),
round(sensitivity_rf,2),
round(sensitivity_rf_tuned,2)
)
Specificité <- c(round(specificity_glm,2),
round(specificity_glm_tuned,2),
round(specificity_knn_tuned,2),
round(specificity_svm,2),
round(specificity_svm_tuned,2),
round(specificity_tree,2),
round(specificity_tree_tuned,2),
round(specificity_rf,2),
round(specificity_rf_tuned,2)
)
AUC <- c("x",
round(auc_glm,2),
round(auc_knn,2),
"x",
round(auc_svm,2),
"x",
round(auc_tree,2),
"x",
round(auc_rf,2)
)
modele_compare <- data.frame(Methode, Erreur,Kappa, Sensibilité, Specificité, AUC)
modele_compare
model_list <- list(logistic = model_glm_tuned,
knn=model_knn_tuned,
rf = model_rf_tuned,
svm = model_svm_tuned,
tree = model_tree_tuned)
results <- resamples(model_list)
summary(results)
Call:
summary.resamples(object = results)
Models: logistic, knn, rf, svm, tree
Number of resamples: 5
ROC
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
logistic 0.8986591 0.9123697 0.9134870 0.9131676 0.9161249 0.9251974 0
knn 0.8999665 0.9130126 0.9130488 0.9120522 0.9157041 0.9185292 0
rf 0.9579029 0.9639111 0.9670451 0.9664253 0.9697597 0.9735076 0
svm 0.8872150 0.9068161 0.9155670 0.9115231 0.9170790 0.9309387 0
tree 0.8428378 0.8480516 0.8529361 0.8525265 0.8568724 0.8619345 0
Sens
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
logistic 0.7978022 0.8017621 0.8215859 0.8133834 0.8219780 0.8237885 0
knn 0.7863436 0.7868132 0.7973568 0.7966559 0.8043956 0.8083700 0
rf 0.8505495 0.8744493 0.8769231 0.8741289 0.8788546 0.8898678 0
svm 0.7841410 0.8017621 0.8043956 0.8098504 0.8237885 0.8351648 0
tree 0.7599119 0.7890110 0.8083700 0.7984083 0.8105727 0.8241758 0
Spec
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
logistic 0.8348018 0.8395604 0.8480176 0.8538742 0.8659341 0.8810573 0
knn 0.8568282 0.8659341 0.8722467 0.8701593 0.8725275 0.8832599 0
rf 0.9120879 0.9186813 0.9207048 0.9207794 0.9229075 0.9295154 0
svm 0.8285714 0.8461538 0.8612335 0.8552094 0.8700441 0.8700441 0
tree 0.7863436 0.7907489 0.8065934 0.8076420 0.8083700 0.8461538 0
bwplot(results)
bwplot(results, metric = "ROC")
bwplot(results, metric = "Spec")
bwplot(results, metric = "Sens")